Kubernetes Autoscaling

Understanding Kubernetes Autoscaling

While operating data in a cluster, the demand for computer resources remains dynamic where in some cases, the resource requirement would be high while in others, it could be drastically low. Allocating the same resources for every situation can lead to massive waste, while manually performing resource adjustments could require much effort. The solution to that issue is Kubernetes Autoscaling. This article will help you to learn about Kubernetes Autoscaling, why it helps, its types, and best practices.

What is Kubernetes Autoscaling?

Autoscaling in Kubernetes is the eradication of manually scaling up or down the resources as per a change in conditions. As the name suggests, it performs the scaling of clusters through automation for better resource utilization and reducing overall costs. It can also be used simultaneously with the cluster autoscaler to utilize only the required resources. Kubernetes Autoscaling ensures that the cluster remains available even when running at a peak capacity. There are two Kubernetes Autoscaling mechanisms.

  1. Pod-based scaling: This mechanism is supported by the Vertical Pod Autoscaler (VPA), and the Horizontal Pod Autoscaler (HPA).
  2. Node-based scaling: The node-based scaling mechanism is supported by the Cluster Autoscaler.

Benefits of Kubernetes Autoscaling

Kubernetes Autoscaling has proven to be beneficial for organizations’ operating clusters. Here are some of the significant benefits you can expect from Kubernetes Autoscaling.

  • Cost Saving: Without autoscaling, the resources consumed in the clusters will either be too much or too low. As autoscaling can adjust the resources as required by the clusters, it ensures they are utilized efficiently and without any waste. With better resource utilization, the overall cost will come down drastically.
  • Reduce Manual Efforts: If you are not using autoscaling, you would need to manually allocate the resources for the cluster whenever the application requires it. Not only will it require a lot of manual effort, but it can also lead to extreme wastes of time. Autoscaling can solve all these issues, reducing many manual efforts required for resource allocation.

Types of Kubernetes Autoscaling:

There are three most widely used types of Kubernetes Autoscaling. The below section will explain all about these Autoscaling types and how it helps in minimizing cluster costs.

#1: Horizontal Pod Autoscaler (HPA):

There are stances when an application faces fluctuations in usage. In that case, the best action is either adding or removing pod replicas. Horizontal Pod Autoscaler deploys additional pods in the Kubernetes cluster automatically when the load increases. It modifies the entire workload resource and scales it automatically as per requirement. Whether scaling up or scaling down the pods, HPA can do it automatically to meet the load.

The Working of HPA:

HPA follows a systematic approach while modifying the pods. It understands whether or not the pod replicas need to be increased or decreased by taking the mean of a per-pod metric value. Afterward, it analyzes whether raising or reducing the pod replicas will bring the mean value near to the desired number. This autoscaler type is managed by the controller manager, and it runs as a control loop. Both stateless apps and stateful workloads can be handled through HPA. 

For instance, if five pods are performing currently, your target utilization is fifty percent, and the current usage is nearly seventy-five percent. In that case, the HPA controller will add three pod replicas in the cluster to bring the mean number near the fifty percent target.

Limitations of HPA:

HPA has a few limitations that should be kept in mind before implementation. One limitation is that you cannot configure it on a Replication Controller or a RecaSet while using a Deployment. Furthermore, it is always advised to avoid using HPA with VPA on the CPU. 

Best Practices for using HPA:

For the best outcome from HPA, experts recommend using the following practices:

  • Custom Metrics: HPA supports pod and object metrics as custom metrics. Using custom metrics as the source for making the right decisions from HPA is an effective way of autoscaling. However, using the right type as per the requirement is the key to getting the desired results. If the team is highly skilled, they can also use third-party monitoring systems to add external metrics.
  • Value configuration for every container: The decisions made by HPA remain accurate only when the values are included for each container. Failing to do so may lead to inaccuracies in making the scaling decisions. The right practice is ensuring that every container’s value is configured correctly.

#2: Vertical Pod Autoscaler (VPA):

In several instances, containers focus on the initial requests instead of upper-limit requests. Due to this reality, the default scheduler of Kubernetes overcommits the CPU reservations and the node’s memory. In this situation, the VPA can increase or decrease these requests to ensure the usage remains within the resources.

In simpler terms, VPA is the tool that can resize pods for efficient memory and CPU resources. It increases the CPU reservations automatically as per the application and can also increase the utilization of cluster resources.  It only consumes necessary resources while it ensures that the pod makes the most out of the cluster nodes. In addition, it can make changes in the memory requests automatically, it drastically reduces the time consumed in maintenance.

Working of VPA:

The basic working of the vertical pod autoscaler consists of three different components, which are briefly explained below:

  • Admission controller: This component overwrites the pods’ resource requests after they are created.
  • Recommender: It calculates the overall target values which will be used for autoscaling and evaluates the utilization of resources.
  • Updater: The updater monitors the pod’s resource limits and checks whether they need updating or not.

Limitations of VPA:

The minimum memory allocation in VPA is 250 MB, which is one of its major limitations. If the requests are smaller, they will be increased to fit this number automatically. Apart from that, it cannot be used for individual pods that do not have an owner. Furthermore, if you want to enable VPA on components, you need to ensure that they have a minimum of two healthy replicas running, or you need to autosize them.

Best Practices for using VPA:

Here are the best practices for VPA that the experts recommend:

  • Run VPA with updateMode: Off: Most veterans recommend running VPA with updatedMode: Off as it allows the user to identify the usage of resources of the pods that will be autoscaled. Doing so will provide recommended memory and CPU requests which can be used later.  
  • Avoid Using VPA and HPA Together: VPA and HPA are incompatible, and they should not be used for the same pod sets. However, exceptions can be made if the HPA is configured to use external or custom metrics.

#3: Cluster Autoscaler

In case you want to optimize costs through dynamic scaling the number of nodes, Cluster Autoscaler is the mechanism for you. It modifies the number of nodes in a cluster on all the supported platforms and works on the infrastructure level. However, due to this reason, it requires permission to add or remove infrastructures. All these factors make it suitable for workloads that face dynamic demand. 

Another action by cluster autoscaler is scanning the managed pool’s nodes to reschedule the pods on other cluster nodes and remove them if found.

Working of Cluster Autoscaler:

The Cluster Autoscaler looks for pods that cannot be scheduled and determines whether consolidating the currently deployed pods to run them on lower node numbers is possible or not. If it is possible, it evicts and eradicates them.

Limitations of Cluster Autoscaler:

Unlike other Kubernetes autoscaling mechanisms, the cluster autoscaler does not rely on memory or CPU usage for making scaling decisions. Instead, it monitors the pod’s requests and limits for memory resources. Due to this process, the cluster can have low utilization efficiency. Apart from that, the cluster autoscaler will issue a scale-up request every time there is a need to scale up the cluster. This request can take between thirty to sixty seconds. However, the time consumed to create a node can be higher, impacting the application performance.

Cluster Autoscaler Best Practices:

Below are the best practices that should be followed while deploying the cluster autoscaler.

  • Use the right Kubernetes Version: Before you begin deploying the cluster autoscaler, you need to ensure that either you have the latest version of Kubernetes or the recommended Kubernetes version compatible with the cluster autoscaler. 
  • Have Resource Availability for Cluster Autoscaler Pod: You need to make sure that resources are available for the cluster autoscaler pod. To do that, you need to define at least one CPU for resource requests made to the cluster autoscaler pod. If this requirement is not met, the cluster autoscaler may stop responding.

Karpenter:

Karpenter is another Kubernetes Cluster autoscaler that is built on Amazon Web Services. This open-source and high-performing autoscaler can enhance the availability of the application and the efficiency of the cluster through rapid deployment of the required resources depending on the varying requirements. 

Licensed under Apache License 2.0, it can work with any Kubernetes cluster in any environment. Moreover, it can perform anywhere, including managed node groups, AWS Fargate, and self-managed node groups. 

Upon successful installation of Karpenter, it analyses the resource requests of unscheduled pods. Afterward, it makes the necessary decisions for releasing new nodes and terminating them to minimize costs and latencies related to scheduling.

Kubernetes – Event-Driven Autoscaling:

Kubernetes-based Event-Driven Autoscaling or KEDA is also an open-source component that helps use event-driven architecture to benefit Kubernetes workload. KEDA scales the Kubernetes deployment horizontally and allows users to define the criteria for autoscaling based on the event source and metrics information. This functionality allows the user to choose from different pre-defined triggers that function as metrics or event sources while autoscaling. KEDA contains two components which are explained below. 

  • KEDA Operator: With the KEDA operator, end-users can scale workloads in/out from zero to N instances through support for Jobs, Kubernetes Deployments, or any custom resource that defines subresource as /scale.
  • Metrics Server: Autoscaling actions like the number of events in the Azure event hub or messages in Kafka topic can be done through metric servers as it exposes external metrics to HPA in Kubernetes. However, KEDA should be the only metric adapter in the system because of the upstream limitations.

How Does Kubernetes Cluster Autoscaler Work?

The Cluster Autoscaler does not function like the HPA or VPA as it does not look at CPU or memory when it activates autoscaling. Rather, it takes action based on events and checks for pods that are not scheduled. If there are any unschedulable pods in the cluster, the cluster autoscaler will commence creating a new node. 

There are instances when the user may have node groups of numerous node types. In that case, the cluster autoscaler will choose the most suitable strategies among the following:

  • Most Pods – Here, the cluster autoscaler will pick the node group to schedule the maximum pods.
  • Random – The default strategy of the cluster autoscaler where a random node type will be picked.
  • Priority – The node group with the highest priority will be selected by the cluster autoscaler.
  • Least Waste – The node group with the minimum ide CPU after scaling-up will be picked.
  • Price – Here, the node group that will cost the minimum will be picked by the cluster autoscaler.

After identifying the most suitable node type, the cluster autoscaler will call the API for provisioning a new compute resource. This action may vary with the cloud services the user is using. For instance, the cluster autoscaler will provision a new EC2 instance for AWS, whereas a new virtual machine will be created on Azure, and a new compute engine will be created on the Google Cloud Platform. Upon completing the compute resource, the node will be added to the cluster so that the pods that are not scheduled can be deployed.

FAQ(Kubernetes AutoScaling)

The primary difference between Kubernetes HPA and VPA is that the former increases or decreases the number of pods, whereas the latter does the same but with the pod resources rather than the number of pods.

In Kubernetes autoscaling, the resource allocation varies as per the cluster requirement. On the other hand, load balancing is about allocating resources equally in every available zone in a region.

The Amazon Elastic Kubernetes Service supports Kubernetes Cluster Autoscaler and Karpenter for Kubernetes autoscaling.

In Blue-Green deployment, two separate but identical environments are created where one is the running environment called Blue and the other is the newer version of the same environment called Green. The users will be using the Blue environment and will not have any idea of the Green environment’s existence unless the traffic is pushed to it. 

Though both exist simultaneously, the Kubernetes will point to the Blue version. The Green version will be used to perform all the different types of tests to ensure that it does not cause issues to the users after deploying. 

Once the tests are complete, the new version or the Green environment can be deployed without causing any downtime, and if everything goes smoothly, the previous version will be discarded, and Kubernetes will now point to the current version normally. 

However, if anything goes sideways and the Green version causes issues, Kubernetes will revert it to the previous version (Blue) without causing any issues to the users.

Related Posts:

  1. Docker Best Practices 2022.
  2. Understanding Docker Components.
  3. Understanding Multi-Tenancy in Kubernetes.
  4. Understanding the Kubernetes Architecture.
  5. All You Need to Know About Containers.
  6. Detailed Guide on DevOps Implementation.
  7. Google has a New Cloud Platform – What Does it Mean for Application Development?
  8. Serverless Computing Options With AWS, Azure, Google Cloud.
  9. Docker Swarm vs. Kubernetes: Comparison 2022
Docker Swarm vs. Kubernetes

Docker Swarm vs. Kubernetes: Comparison 2022

Presently, legions of organizations rely on containers for grouping all the key dependencies in a single package. Regarding container orchestration tools, two names are often heard; Docker Swarm and Kubernetes. Both are considered among the top container orchestration tools, but which is better? This article will compare (Docker Swarm vs. Kubernetes) both tools and will elaborate on the criteria to pick the most suitable tool for your organization.

Docker Swarm vs Kubernetes

What is Docker Swarm?

Docker Swarm is an open-source container orchestration platform that helps manage Dockerized containers. Though it may sound similar to Docker, Swarm is renowned for its simplified usage and setup. A Docker Swarm cluster contains nodes, services, tasks, and load balancers. This platform allows apps to perform on several nodes that share a single container. In simpler terms, the Docker swarm is used to deploy, manage, and scale a cluster of nodes on Docker efficiently.

Advantages of Docker Swarm:

Knowing about Docker Swarm’s advantages will help you know more about the platform and make wise decisions.

  • Docker Swarm is lightweight and easy to use for new users.
  • The learning curve is smooth, making it a preferred choice for beginners.
  • It is easy to install and set up.
  • Supports all the major operating systems.
  • It uses the same command line interface as Docker Engine.
  • Being created by Docker, it is compatible with all the existing Docker products.
  • Suitable for small and less complicated systems.

Disadvantages of Docker Swarm:

There are several disadvantages of Docker Swarm that are mentioned below:

  • Not suitable for complex infrastructures
  • Customization in Docker Swarm is limited
  • Due to the tie-in with the Docker API, the functionality of this platform is limited
  • Community is smaller as compared to other platforms

What is Kubernetes?

Also called K8s or Kube, Kubernetes is an open-source platform for managing containers. Due to its features, including self-healing, load balancing, rollback, and configuration management, Kubernetes is one of the most popular container orchestration platforms. Kubernetes allows DevOps teams to deploy, manage, or schedule apps through clusters. A builder and worker nodes are further segregated into namespaces, pods, config maps, and many others, making it a complicated platform.

Advantages of Kubernetes:

Kubernetes is a feature-packed tool that comes with several benefits, including:

  • Suitable for complex systems or infrastructures.
  • Compatible with all major operating systems.
  • A vast community built with years of existence in the industry.
  • A perfect blend of features that can fulfill all the organization’s needs from the tool.
  • Support for a broad range of third-party tools and integrations.
  • Cloud Native Computing Foundation support.
  • Comes with a unified set of APIs.

Disadvantages of Kubernetes:

Kubernetes come with certain limitations you should know before making your final decision.

  • A complicated learning process makes it unsuitable for beginners to learn this tool.
  • Sometimes, it needs additional tools to accomplish tasks.
  • The installation process is complex and time-consuming.
  • Massive for small teams and individual developers.

Docker Swarm vs. Kubernetes : Comparison

Even though Docker Swarm and Kubernetes are both container orchestration platforms, there are still several ways in which they differ. The following table section is a detailed comparison between the two which will help you know more about them. 

FeatureDocker SwarmKubernetes
1. Installation.Before you can use any of the platforms, you need to install them. Docker Swarm is famous for its quick and easy setup on a system having Docker Engine. You only need to assign the IP address to hosts, assign a managed node, and open the protocols and ports within the hosts, and the setup will be done. Due to its easy installation, Docker Swarm is preferred by teams that lack high technical skills.On the other hand, installing Kubernetes is a significant task that needs pre-planning. Here, the team should install the Kubernetes Command Line Interface, Kubectl, which varies with the operating system on which it is installed. For instance, curl can be used to install K8s on Linux, whereas it can be installed on macOS and Windows through Homebrew and Gallery package manager, respectively.
2.Dashboards.Dashboards allow users to have a better user interface while using the platform. Docker Swarm does not come with a built-in dashboard. However, it does not say that it cannot have a GUI. This platform can be integrated with a third-party tool like Swarmpit or Dockstation for a GUI.
On the contrary, Kubernetes comes with built-in dashboards through Web UI. Not just controlling the clusters, this GUI can help Kubernetes users in deploying applications on a cluster, monitoring and managing clusters, and viewing error logs.
3.Deployment.Regarding the deployment factor, the Docker Swarm allows users to deploy applications through predefined Swarm files for the desired state of the App. To deploy an application, the user needs to copy the Docker Compose or YAML file at the root level. With this file, the user can make the most out of the several node machine capabilities through which they can run containers on several networks and machines.Deployment in Kubernetes requires describing a declarative update to the App state when the Kubernetes Pods and ReplicaSets are updating. Once describing the desired state of the pod, the controller will change the current state of the pod. With Kubernetes, the users can define different aspects of the application’s lifecycle. However, it requires a lot of skill and is highly complicated to perform.
4.Availability.Among all, one of the best features of Docker Swarm is its availability controls. Moreover, duplicating microservices is uncomplicated as well. Sometimes host failure can occur in Docker Swarm. In that case, this platform allows manager nodes to move a worker node to any other resource as desired.For app availability, Kubernetes provides two different topologies. The first is using an external etcd object for load balancing and handling the control plane nodes separately. The other option is to co-locate the etcd object with every available cluster node during a failover using a stacked control plane node. Apart from that, K8s come with self-healing and flaunt-tolerant capabilities as well.
5.Scaling.Even though both Docker Swarm and Kubernetes allow the user to scale up or down their infrastructure as per their requirements, the way of performing the task is different. In Docker Swarm, users must perform this task manually through Docker Compose YAML templates.On the other hand, Kubernetes comes with automated scaling, which can auto-scale the cluster and pod level depending on the current traffic. In theory, K8s seem to be better at scaling down. However, Docker Swarm’s scaling down process is faster as it does not have any complex framework that slowdowns the entire process.
6.Networking.Both Docker Swarm and Kubernetes follow different models for networking. Docker Swarm will create two different types of networks for a node in a cluster. One network will highlight an overlay of the network service, whereas the second network type will create a host-only bridge for each container.In the case of Kubernetes, the networking model is a bit simple where peer-to-peer pod communication takes place. All such pods communicate with each other, and it needs two controller managers, which will be used for exposed services and for providing IPs to pods.
7.Monitoring.Monitoring in Docker Swarm only offers Docker’s event and server log tools, which are basic. It is complicated in Swarm as the cross-node objects and services are in large volume. Considering that fact, the user needs to have third-party extensions like cAdvisor or Grafana for better monitoring features.Kubernetes comes with monitoring and logging functionality built-in. The monitoring functionalities of this tool can evaluate individual containers, pods, and services and observe the cluster’s behavior. Though the in-built features of this platform can fulfill all the major requirements, still if the users want highly detailed metrics, they can integrate additional tools fulfilling their needs.
8.Load Balancing.Load Balancing is an essential feature of handling unexpected loads efficiently. Docker Swarm comes with automatic load balancing, making it uncomplicated.On the contrary, Kubernetes does not come with any automatic load-balancing feature. However, third-party tools can be integrated with this platform that can enable automatic load balancing.
9. Security.Security can be a significant concern for some users using a container orchestration platform. Docker Swarm depends on network-level security through authenticated TLS, where security certificates are frequently rotated between nodes.In the case of Kubernetes, it offers enterprise-grade security controls like pod security policies, SSL, RBAC authorization, secrets management, and many others. Furthermore, commercial cloud-native security tools can further enhance the platform’s security.

Why Choose Docker Swarm as your Container Orchestration Tool?

Docker Swarm is developed by Docker, and it builds on Docker with several instances of the Docker Engine. With instances of Docker engine, it needs minimal additional setup after installing Docker on the system. Due to this, Docker Swarm can be installed and be ready to run in a concise duration. Considering that fact, Docker is the right container orchestration platform for all those users who want easy installation and setup without compromising the primary features.

Why Choose Kubernetes as your Container Orchestration tool?

Kubernetes provides the utmost flexibility while managing containers as a container orchestration platform. You can modify the platform in any way you like, and it will allow you to do whatever you want to do. However, with great flexibility comes a great learning curve. Undeniably, the user needs to learn about this platform to become comfortable and make the most out of it. 

Being the most popular and open-source container orchestration platform, it has a herculean community that can help you provide information, find solutions to common issues, and the necessary support. 

Though the installation is a bit complicated, a skilled professional can fulfill this task quickly. Conclusively, Kubernetes is for all those users who are open to learning and want the utmost flexibility and all the major features a container orchestration tool can offer.

Which One is the Better Option?

The best option depends on the organization’s needs when compared to other IT tools and platforms.

Docker Swarm is easy to set up, can be integrated with Docker tools, and works effectively with small workloads. On the other hand, Kubernetes is a bit complicated, but it is currently used by legions of organizations and is proven effective for complex infrastructures. If your team wants an easy-to-install and use platform, Docker Swarm is a perfect choice. On the other hand, if the infrastructure is complex and the team is skilled in handling a complex tool, they should surely go with Kubernetes. 

Container orchestration is a pivotal yet complicated action. If done incorrectly, it can cause catastrophic damage to the containers and the application. ThinkSys Inc. is a pioneer in providing container orchestration services to numerous organizations all around the globe with primary clients in the USA and Europe.

Global Containerization Services Offered By ThinkSys

ThinkSys Inc. has a skilled talent pool having years of experience in working with containers and on different platforms. Below are the container services offered by ThinkSys:

Frequently Asked Questions

Kubernetes has most of the container orchestration platform’s market share, due to which many may believe that Docker Swarm is no longer used. However, many organizations like Anthem, Wells Fargo, and UnitedHealth Group still use Docker Swarm as their platform for container orchestration.

Undeniably, the learning curve of Kubernetes is steep, especially for a beginner. If you plan to learn Kubernetes, then you do not need to learn Docker Swarm. It might be challenging initially, but it will provide an excellent outcome afterwards.

Docker is a containerization platform that allows users to package their applications into containers. Kubernetes can indeed be used without Docker and will achieve the expected outcome. However, using Docker will enhance its features, and professionals recommend using K8s with Docker.

Our Related Posts:

  1. Docker Best Practices 2022.
  2. Understanding Docker Components.
  3. Understanding Multi-Tenancy in Kubernetes.
  4. Understanding the Kubernetes Architecture.
  5. All You Need to Know About Containers.
  6. Detailed Guide on DevOps Implementation.
  7. Google has a New Cloud Platform – What Does it Mean for Application Development?
  8. Serverless Computing Options With AWS, Azure, Google Cloud.
devops consulting services

Why DevOps Consulting is Gaining Immense Pace in the Industry?

The software development industry is highly competitive, where every player makes their best efforts to release software before anyone else. With that desire in mind, DevOps is therefore the practice followed by legions of organizations for a common goal: to deliver high-quality software quickly. DevOps is all about collaboration between different teams on different methods using unique tools to optimize the software development lifecycle. When it comes to DevOps implementation, organizations have two options; get an in-house team or consult professional DevOps service providers. Being a herculean practice, not every organization can implement its own internal DevOps culture, especially in startups. So, the best option in the present scenario is to work with external DevOps Consulting Services.

Devops Consulting Services

Not just startups, but also well-established organizations are now migrating towards DevOps Consulting as it is proven to be cheaper than recruiting and managing an in-house team. Nations including the United States, Germany, Turkey, Canada, UK, Brazil, India, and many others have established themselves as the leaders in providing DevOps Professional services. The DevOps market industry is valued at over USD $6.73 billion and is expected to reach around USD $26.3 billion by 2028, with a compound annual growth rate of over 20% between 2022-and 2028. Considering those numbers, if any organization lags in implementing DevOps, it will lose the software development race in the long run.

Cost of DevOps Consulting Services in Different Regions

Calculating the overall cost of DevOps Consulting is the foremost consideration when picking a provider. The cost of DevOps as a service will vary depending on the geographical location of the provider.

RegionCost per hour
USA$100-150
UK$80-120
Australia$100-150
Asia$50-100
India$25-75

These were the average cost per hour in different countries established as the leading DevOps service providers. The information above shows that the USA and Australia are relatively expensive compared to India or other countries in Asia. However, a country providing service at a cheaper or costly rate does not signify its relative quality. A region’s overall cost is influenced by factors like economy, per capita income, and demand for service. India in particular is one of the most affordable regions for DevOps Consulting, as demonstrated by the legions of organizations who regularly connect with companies in India for this specific service. Not just because of the pricing, but for its high quality of service, too. 

Advantages of DevOps Consulting Services

  1. Access to DevOps Experts: Getting reliable DevOps engineers may seem tricky when an organization’s business model is not at all about best-practice software architectures. A reputed DevOps Consulting provider will be equipped with experienced and reliable DevOps professionals, ensuring their clients get the best service. Organizations having a business model other than cloud software architecture can attain the best DevOps service through external consulting without worrying about having an in-house team for the same purpose.
  2. Flexibility to Get a Suitable Professional: DevOps Consulting companies provide an individual or a team of DevOps professionals to an organization. Sometimes, that professional’s working approach may not align with the organization. In that case, that organization has the option of letting the consulting company know about any concerns, who will in turn assign a new professional better suited to their goals.
  3. Better Service at a Lower Cost: Hiring an in-house DevOps team involves wages, tools, training, and infrastructure expenses. Even after investing, there is no guarantee that the team can provide the results you were hoping for. In contrast, when you consult DevOps professionals, you are not only guaranteed that you will get the best DevOps service for your organization but at a lower overall Total Cost of Ownership (TCO) as well. You are safe from spending on training, tools, and infrastructure required for implementing DevOps in your organization.
  4. High Deployment Speed: DevOps is all about optimizing the software development life cycle through Continuous Improvement and Continuous Development (CI/CD). Every DevOps Consulting team is bound by a contract by which they have to accomplish specific goals and/or the deployment of an App or system. Furthermore, the consulting company will handle the entire project management responsibility, ensuring that high deployment “velocity” is maintained.

Cons of DevOps Consulting Services

  1. Possible Communication Gaps: Due to cheaper costs, many organizations from the U.S.A., UK, and Australia prefer DevOps consulting from Asia and India. However, there can sometimes be communication gaps between the client organization and a DevOps vendor, which require specific communication and collaboration processes to be established. Even after that, there can still be some instances where issues can arise due to communication and/or cultural gaps.
  2. Too Much Reliability on Vendors: When you use DevOps consulting, you come to depend on the vendor to handle your organization’s DevOps practices. However, there can be instances where a vendor can be responsible for shutting down a client’s business. Even though you will be notified before that happens, it becomes an additional expenditure. On the other hand, if the DevOps consulting service provider is not reliable or is inexperienced, your application security could potentially be compromised. To avoid such issues, it is best to pick a reliable service provider with years of proven experience.

DevOps-as-a-Service (DaaS)

DevOps-as-a-Service (DaaS) is about shifting the entire DevOps tools and infrastructure to the cloud. When it comes to DevOps Consulting, then DaaS can be used by companies so that they can access their client’s current tools and practices, initiate cloud migration and handle the entire delivery pipeline to the cloud. Furthermore, to sustain CI/CD and continuous testing, which are the pillars of DevOps, DaaS provides the development tools in a single cloud-hosted kit. All these actions combined make DevOps-as-a-Service reliable for improving the process’s performance, scalability, and automation.

DevOps Consulting Services vs. an In-House Team

  • Management: Managing a crucial team like DevOps is a challenging task. If the entire DevOps team is in-house, then typically managing them directly is not a major task – although it is a task that must be done and comes with an associated supervisory cost. However, seamless ease of management of a remote DevOps team is not the case with professional services/consulting. Remote teams can either be in the same city or in other parts of the world. Nevertheless, while technology has brought everyone closer, managing a DevOps team remotely is still a unique, albeit manageable, challenge as compared to an in-house team.
  • Delivery Speed: No matter how fast the team has started working on a software idea, rapid working will not matter if there are delivery delays. One of the best things about DevOps Consulting is faster delivery. Every consulting company strictly endeavors to meet deadlines, and takes every measure possible to ensure fast product delivery. Conversely, the biggest issue with in-house teams is absent staff. Whenever a professional is absent, finding a replacement is typically not possible. However, professional vendors ensure that you always have ample professionals available and provide you with a substitute in case of any staff absenteeism.
  • Talent Pool: When a company hires an in-house DevOps team, they can choose from a vast range of professionals and shortlist them as per their skills. Once they have shortlisted the candidates, they are bound with them for the long term. There is nothing wrong with long-term employees, but it will hinder your access to outside talent. Professional DevOps services do not come with such complications as you can request to replace any existing team member with a new one if you are unsatisfied. In other words, you will access a broad range of talent when you use DevOps Professionals.

DevOps Consulting Services Best Practices

Understanding the best practices of DevOps professional consulting before opting for it helps get the right results per your organizational goals. With that in mind, here are some of the best practices for DevOps professional services.

  • Determine the form of Required Professional Services: There are legions of DevOps professional vendors offering all kinds of DevOps-related services. Before looking for a vendor, it is best to first determine what form of professional you are looking for. Whether you need someone to augment your existing DevOps team or if you want an end-to-end DevOps solution, your goal will help you filter the organizations that will help you.
  • Find a Reliable Professional Services Vendor: Picking a professional vendor will directly impact your product quality and software delivery. Always choose a DevOps professional company that aligns with your goals and has a proven track record of providing similar services. You can reference their previous projects and clients and learn about their total experience, their level of scalability, and feedback from their previous clients, among other factors.
  • Maintain Communication: As stated before, a communication gap is one of the biggest challenges in DevOps Consulting. Being a significant yet complicated task, many organizations ignore maintaining steady communication with the consulting company, due to which the project quality is compromised. The best practice is always to maintain communication to avoid such a scenario. Ask different questions and answer the vendor’s questions as well to ensure that both parties remain on the same page.
  • Never Underestimate Project Management Tools: Using project management tools is among the most underrated yet essential practices. When an in-house team and an external consulting team are working on the same project simultaneously, or a third party is working on a project, having a common project management tool will easily solve all the management issues. Several free and paid project management tools are present in the market, which you can choose per your organization’s and project requirements.

ThinkSys DevOps Consulting Services

A reliable DevOps professional vendor can take your DevOps approach to the next level. ThinkSys is among the leading DevOps companies in the world, offering software development, and many other QA-related services. ThinkSys has been working with organizations worldwide to provide the best-in-class DevOps services without any hindrance or interruptions. The perfect blend of software development services and DevOps ensures the right continuous delivery and integration in your organization. Here are the different DevOps Consulting services that you can get at ThinkSys:

Our Professional DevOps Consulting Services:

Our DevOps Strategy

At ThinkSys, we follow a streamlined approach toward DevOps, keeping quality standards in mind. Below is the detailed strategy we use while providing our DevOps services.

  1. Assessment: Before moving further, the team at ThinkSys will learn and come to intimately understand the existing DevOps culture of your organization. Details like existing agile methods, process of workflow automation, microservice architecture, and way of delivering software components will be understood by our team to identify the necessary work required.
  2. Create a Plan: Once the team understands your existing DevOps culture and infrastructure, they will create a future course of action roadmap. This roadmap will be a blueprint of the actions and goals that will be achieved in the future to reduce issues, cost, and development time while increasing the frequency of new software version releases.
  3. Execute the Plan: With all the planning successfully completed, the team moves on to execute the plan in the most effective ways. Our team begins to implement Continuous Integration and Continuous Deployment (CI/CD), and version control to boost stability with the environment.
  4. Optimize the Plan: Our team will continue identifying the best practices while implementing the plan to enhance performance and scale the application. This optimization of the plan will help in the long run and will also automate the verification to add updates to the plan.
  5. Continuous Support: ThinkSys believes in providing continuous support to every client. Even after the completion of the process, our team continues to provide support for solving any issues and queries with the projects. The dedicated support team and different DevOps teams maintain stable communication to solve any issue you may be facing.

At ThinkSys, we are always eager to onboard any new client for DevOps consulting or to solve any DevOps issue. Our teams are proficient and skilled in following the best DevOps practices, ensuring fast delivery and smooth CI/CD pipelines.

10 Mobile App Testing Challenges

Let us Solve Your DevOps-Specific Challenges

Want to leverage the DevOps approach to deliver reliable software quicker?

FAQ(DevOps Consulting)

The DevOps market size is expected to grow to over $26 billion by 2028 from the current $6 billion. DevOps directly reduces the SDLC, ensuring faster delivery of software and enabling you to gain an edge over the competition. With this significant factor, DevOps is surely worth it.

DevOps as a Service (DaaS) will switch the traditional collaboration between operations and development teams to the cloud to automate several processes for quick software delivery.

The first step you need to make to get DevOps Consulting is to understand why you want this service. Identify your requirements, goals, and expectations from DevOps Consulting providers. Afterwards, start searching for consulting companies that perfectly align with your business goals and ideas.

DevOps is all about embracing collaboration between development and operation teams for optimizing the SDLC. Along with certain practices, it is crucial to use the best DevOps tools for the best outcome. Here are some of the top DevOps tools used in the industry presently:

  1. Slack
  2. Jenkins
  3. Docker
  4. Phantom
  5. Ansible
  6. Github
  7. CloudForestX.

DevOps Consulting comes with numerous benefits, but one of the biggest ones is letting your organization focus on its core agenda without distraction. The Consulting team will handle the entire DevOps culture independently, allowing the organization to work without third-party interference.  Apart from that, it is highly cost-effective, saves time, and ensures that the goals are always met on time. 

devops implementation guide 2022

Detailed Guide on DevOps Implementation

The past decade has witnessed immense developments in the digital industry. IT has come a long way toward software-driven tasks, from doing tasks manually to relying on automation. As one of the most significant industries, software development is all about writing-testing and deploying the software as quickly as possible.

When it comes to minimizing the software development lifecycle, DevOps is the name that comes to light. Without a doubt, DevOps is used by the development teams of several tech giants. However, implementing DevOps requires the right practice, strategy, cultural shift, and tools. This article will act as your detailed guide on DevOps implementation and how to do it the right way. 

What is DevOps?

Initially, a development and an operations team were responsible for continuous integration. These teams worked on their individual goals and communicated only when necessary, leading to a significant communication gap. 

DevOps is a cultural shift in an organization that enhances collaboration between the development and operations teams where they both share the responsibility while working on software development. The collaboration between the two helps in reducing the software development lifecycle and promotes continuous integration and continuous development.

Benefits of DevOps :

Before moving further with the implementation, it is crucial to understand the actual benefits of DevOps. Explained below are the primary perks of implementing DevOps in your organization.

  1.  Faster Deployments: The foremost reason why DevOps has become increasingly popular in the present time is due to its capability to ensure faster software deployments. Be it a new software release or an update, DevOps can help in satisfying customers by providing them with the software rapidly.
  2. Less Error, More Innovation: DevOps promotes automation in the organization’s culture. Automation ensures minimal errors due to human intervention, making the software less erroneous. When human personnel is not working manually on the process, they have more scope for innovation and can frame new ideas to improve the program.
  3. Better Product Quality: DevOps is all about collaboration between the development and the operations teams. Both the teams not only work towards delivering the product, but they focus on the feedback aspect as well. The continual feedback allows the teams to make significant improvements to the software.
  4. Cost-Effective: One of the biggest reasons organizations prefer implementing DevOps is that it can cut down production as well as management costs of the departments. Unlike the traditional method, every individual is responsible for developing and improving the software, bringing maintenance and new updates under a single roof.

Step-by-Step DevOps Implementation:

Having a clear DevOps implementation guide helps enterprises eradicate hurdles that can hamper not just the initial implementation but future actions of DevOps. With that in mind, there should always be a strategy to implement DevOps in your organization. Considering that fact, here is a detailed guide on how an organization can implement DevOps efficiently.

Step 1: Analyze Pre-DevOps Situation

DevOps brings significant cultural changes to an organization, making it highly challenging. Replacing the existing methods with new ones is always difficult for an organization. Due to this, the first step in implementing DevOps is to analyze the current state of your organization. 

Assessing your pre-DevOps situation will allow your teams to understand what they currently have and what they want to achieve in the future. Furthermore, it will enable your organization to assess the current resources and the changes that they need to bring to implement DevOps.

Step 2: Develop DevOps Mindset and Culture

As stated before, DevOps is all about the cultural shift in an organization as it promotes collaboration, communication, and transparency between different teams, especially development and operations. Not having these three mindsets within the teams can lead to inevitable chaos that can hinder the software development lifecycle. 

It is worth noting that the organization should develop a DevOps mindset before it can begin its implementation. Having clarity on the expectations and the culture will allow your organization to develop a robust action plan and will also prepare your organization for further processes. Once all the teams have the right DevOps culture and mindset, they can proceed further to the next steps in the DevOps implementation plan.

Step 3: Determine the DevOps Process

With a defined DevOps process, you can enhance continuous development, testing, and infrastructure provisioning. The key to a successful DevOps implementation is filling the gap between different teams. Here are the phases that will help in bridging this gap effectively.

  • CI/CD: Continuous integration and continuous delivery are the two crucial DevOps practices. CI is about merging the code changes to the developers’ central repository, and CD is about automating the application delivery to the desired infrastructure environment. With CI/CD, organizations can act depending on the consumer requirements, ensuring they maintain the utmost quality while delivering software.
  • Continuous Testing: Continuous testing is also an integral part of CI/CD, which allows the team to maintain high-quality software and deliver them to the users. Feedback is another aspect of continuous testing that further contributes to improving the software.
  • Continuous Deployment: The next part of the DevOps pipeline is continuous deployment which focuses on deploying and distributing the software to the final users. Continuous deployment is successful by using the right set of tools and scripts, which allows the developers to deploy the code on any server they want and whenever they want. 
  • Microservice Architecture: Microservice architecture is when small services are modeled near complicated applications, allowing the delivery team to manage individual services. With this architecture, the entire testing, development, and deployment process is simplified drastically. As small deployable services are modeled and focused, the crashing of a service does not influence other aspects of the application. Even though microservice architecture is a new trend in software development, it has still managed to be implemented in modern industries.
  • Container Management: Containers have become popular to package application source code, dependencies, configuration files, and libraries in a single object. Several containers can be deployed as clusters for the deployment of applications. In addition, they promote isolation between applications and ensure that the deployed applications use only allocated resources.

Step 4: Pick the Right Toolchain

Having the right DevOps toolchain is essential to get better control, robust infrastructure, and customized workflow. When picking the right toolset for DevOps end-to-end implementation, you need to consider your organization’s requirements and the compatibility with the existing IT environment and the cloud provider. 

A DevOps toolchain can be determined by understanding different software development life cycle stages. Each stage has multiple tool options to choose from, and there should be a dedicated tool for every stage.

  1. Planning: Planning is the stage where requirements and business value are defined. Being the blueprint of the future course of action, this stage is considered one of the most crucial in the development process. Jira and Git are the top tools used for planning in DevOps.
  2. Coding: Coding is the phase where the actual code writing and the software design process occur. GitLab, Stash, and GitHub are the popular tools used in this DevOps phase.
  3. Software Build: In this phase, the developers will use automated tools to manage different software versions, which will be packaged for a future release. Docker, Chef, Gradle, and Puppet are the tools that can be used in this phase.
  4. Testing: Before releasing the software, it should undergo continuous testing to ensure its utmost quality. Tools including JUnit, Selenium, and TestNG are used in this phase to get the best outcome.
  5. Deployment: Once the testing is complete and the software pushed forward from the testing phase, the next part includes managing, scheduling, and automating product releases to production. These tasks can be achieved with tools like Kubernetes, Docker, Jira, and Jenkins.
  6. Monitoring: DevOps is a continuous process that goes on even after deployment. The next phase of DevOps is monitoring, where information about several issues is attained after its final release. Tools like New Relic, Wireshark, and Nagios can help in this phase.

All these phases above are highly important in the DevOps implementation plan. The tools mentioned in these phases are industry-leading and are used by several tech goliaths. However, it would be best if you found the tool that will work for your organization so that you can get the best results from the toolset.

Step 5: Security Practices

Security is another aspect of the DevOps strategy roadmap that influences the future culture of the organization. The right security will safeguard the DevOps environment through strategies, policies, and technology. 

With that in mind, experts always recommend implementing security in all the DevOps lifecycle steps, also called DevSecOps. As the batches of code are pushed frequently, and multiple times, security teams may not cope with reviewing the code. Due to this, DevOps output may have certain security vulnerabilities. The following actions will ensure that the DevOps environment remains secure. 

  • CI/CD practices should come integrated with security so that the security team can segregate apps into microservices and make security reviews easier.
  • Keep an eye on access to other users by using privileged access management to have better control.
  • Implement automation in DevOps security tools.
  • Embrace continuous monitoring to identify any security vulnerabilities at any stage during software development.
  • Build robust yet transparent security policies.

Step 6: Measure DevOps

You want to implement DevOps in your organization to get better quality and performance from your application. Even after its implementation, you need to measure and analyze different metrics that align with your organizational goals to have transparency over the software development. 

To achieve this goal, DevOps’ continuous implementation should include the necessary metrics. Here are the common metrics you should measure to understand your DevOps performance.

  • Lead Time to Changes: Lead time to changes is the metric that showcases your organization’s responsiveness to your users’ requirements. All the changes implemented in the deployment should be noted to determine the organization’s responsiveness.
  • Deployment Frequency: Deployment frequency is one of the most important metrics that depicts the effectiveness of DevOps and CI/CD pipelines. This metric calculates the frequency at which the organization deploys a code to the development, test, and production environment. It allows the teams to understand the process’s efficiency and the team members’ performance while deploying releases. of the team members while deploying releases.
  • Mean Time to Recovery: Mean time to recovery is the metric representing the time consumed to recover from a failure. Every team’s goal is to reduce MTTR as much as possible to provide the best experience to the user while using their program.

Step 7: Have a Competent Product Team

Every DevOps team should be cross-functional so that it can handle every type of situation effectively. Both operations and software engineering skills should be present in team members to optimize the product delivery. 

In addition, qualities like critical thinking, debugging issues, proficiency in using DevOps tools, and ability to grasp quickly should exist within the DevOps team to have smooth Azure DevOps implementation in an organization.

Best Practices for Azure DevOps Implementation Plan:

Understanding the basic DevOps implementation from scratch is surely a step in the right direction. However, certain practices should be followed to attain the maximum results. Below are some of the best practices for DevOps implementation that you should know.

  1. Understand the Needs: One of the biggest questions you need to ask yourself before implementing DevOps is why you need it and what your infrastructure needs are. Your answer should always be related to your business goals, and the need for its implementation should be business-driven. In other words, rather than doing it because it is in trend, you should implement DevOps because you need it. 
  2. Start Small and Scale Afterward: Initially, many organizations make the mistake of executing the plan on a large scale. Not only will it consume more resources, but it increases the probability of failure. Rather than that, the best DevOps practice is to target a faster and small release cycle and then scale the deployments. This will allow your team to learn and improve during the initial implementation of DevOps.
  3. Choose Compatible Tools: Sometimes, you may pick tools that you find feature-rich. Though you may find it the right practice, some tools may not be compatible with each other. They will surely get the job done, but your team may have to put in some extra effort. Make sure that the tools you use for different phases are compatible with each other. 
  4. Document Everything: Documentation is one of the most important factors of DevOps. Whether it is the initial implementation or regular working with DevOps, you should document the DevOps strategy, including your reports, change management, infrastructure, and other aspects. Documenting will ensure that you can analyze the issue whenever you face it and take the right measures to eradicate it.
  5. Embrace Automation: Automation and DevOps are two terms that are often referred to together. With automation, the entire DevOps can be implemented faster. In addition, automation can be used in databases, code development, networking changes, and many other actions. Combined with the right tools, automation can save a lot of time, effort, and overall costs.

How Does ThinkSys Help in DevOps Strategy and Implementation?

Undeniably, understanding the DevOps implementation process is surely the way to go towards accepting DevOps for your organization. However, sometimes, merely having the information might not get the job done with utmost efficiency. In that case, having professional assistance for creating an Azure DevOps implementation plan for your organization helps in the long run. 

ThinkSys Inc. is a renowned name for providing different DevOps services for organizations that will help in the implementation roadmap and in every phase of DevOps. Rather than using a common DevOps strategy, our professionals will analyze your organization and define a custom roadmap that will suit your needs. 

Furthermore, our DevOps agile methodology ensures continuous integration and continuous deployment, making your software ready for delivery. Below are the different DevOps implementation services offered by ThinkSys Inc.

FAQ(DevOps Implementation)

A DevOps tool is an application that assists in automating the software development process by enhancing collaboration and communication between different teams. Some of the most widely used DevOps tools are:

  • Jira
  • Kubernetes
  • Docker
  • Puppet
  • GitLab
  • Bitbucket
  • Jenkins
  • Xray
  • Slack

A DevOps lifecycle is the blend of several phases of continuous software development, monitoring, testing, deployment, and integration. Having a well-defined DevOps lifecycle ensures that DevOps implementation is smooth and effective.

When it comes to releasing or updating applications, organizations can follow the five different components of DevOps called C.A.L.M.S. These are also referred to as the five pillars of DevOps and these are explained below:

  1. Culture: Having a cultural shift is one of the biggest changes that DevOps requires. The entire team should have the mindset to have a cultural shift while focusing on the common goal.
  2. Automation: Automation should be implemented for as many tasks as possible to reduce human intervention and the possibility of errors.
  3. Lean: Lean practices should be followed for effective testing while keeping the infrastructure and other aspects minimal. Additionally, code deployments should also follow the same practice.
  4. Measurement: Every release should be monitored and the metrics should be measured frequently to identify any issue. Frequent measuring and monitoring allow the team to find and fix issues quickly. 
  5. Sharing: Ideas, thoughts, and experiences should be shared with other team members to ensure optimal communication and collaboration. 

DevOps lifecycle has seven different phases which are explained below:

  1. Continuous Development: This is the first phase of DevOps where planning and coding take place. 
  2. Continuous Integration: In this phase, the developers commit changes to the code frequently and build the commit. In addition, integration testing, code review, unit testing, and packaging are parts of this phase.
  3. Continuous Testing: Here, the developed software is tested continually for bugs using testing tools. Tools like Selenium can be used for automated testing, saving a lot of time.
  4. Continuous Monitoring: This is the phase where the teams monitor the way software is used and processes the data to determine the trends and issues.
  5. Continuous Feedback: Improving the app development is done by attaining constant feedback between the development and operations team. 
  6. Continuous Deployment: The code is deployed continuously to the production servers with the help of certain tools like Puppet, Chef, and Ansible.
  7. Continuous Operations: This phase is all about the continuity of tasks achieved by automating the release process. 

When it comes to the success of DevOps, then there is no single action that can guarantee success for every organization. The key is to find the right strategy that will work for the success of your organization. However, the actions like the increased collaboration between technical and non-technical teams, using the right toolset, minimizing the communication gap, and getting professional assistance are considered the foundation for DevOps success.

Implementing DevOps in an organization requires a dedicated strategy along with the right tools. Either you can create your strategy or take professional assistance from professionals like ThinkSys who can not only help in creating the right DevOps implementation strategy but the entire roadmap including different phases of DevOps.

Deployment and implementation are not the same concepts with respect to DevOps. In DevOps, implementation is creating and executing plans to integrate DevOps into an organization’s culture. On the other hand, DevOps deployment is updating an application’s code on your servers Both the terms have a different meaning in DevOps.

Latest Posts:

Docker vs Kubernetes

Docker vs Kubernetes: Which is the Better Choice as a Container Management?

With the rapidly rising usage of containers, tons of tools and technologies have emerged. Even though picking the right tool for container orchestration and management is about individual preference, Docker and Kubernetes have become the most widely used container technologies. 

Though the primary task of both these technologies is somewhat similar, there are several dissimilarities between them. Being unaware of the differences between the two, users may not make the right choice in picking the preferred container technology. In this article, you will learn all about the differences between Docker and Kubernetes, allowing you to choose the right one for your organization.

What is a Container?

Before knowing about the two major technologies, it is crucial to understand what a container is. A container is a software package that includes all the essential elements of software, allowing it to function in any environment. These containers function by virtualizing the OS and can function from a public or a private cloud. 

Containerization has become widely popular within the development teams as it helps deploy software efficiently and allows the teams to move faster than ever. Each container will have an entire runtime environment, including the software, its libraries, configuration files, and all its dependencies.

Docker vs. Kubernetes

What is Docker?

Docker is a lightweight containerization technology that helps in automating the application deployment and management in containers. With Docker, developers can automate the entire infrastructure, reduce resource utilization, and isolate the application to ensure that no other application can influence it. 

Command-line interface tool and the container runtime are the two different components of Docker. The former helps in implementing instructions to the Docker runtime whereas the latter is all about creating and running containers on the operating system. 

Pros of Docker:

  1. Isolation: Docker allows developers to create containers in isolated environments. Irrespective of the application deployment, consistency will always be there whenever the container is scaled, ensuring utmost productivity. As the entire notion consumes less time, the saved time can be utilized to deploy new and effective features.
  2. Portability: One of the biggest perks of using Docker is its portability. After testing your containerized application, you can deploy it to multiple systems, and it will perform the same when you test it in every system where Docker is installed and running.
  3. Scalability: Whenever you need new containers for your application, you can easily create them through Docker. Furthermore, the container management options in Docker have proven to be effective while using several containers simultaneously.

What is Kubernetes?

Developed by Google, Kubernetes is a container management software that helps in managing containerized applications in cloud, physical and virtual environments efficiently. Kubernetes allows applications to run on clusters of a hundred to a thousand individual services. 

It functions in a multiple container architecture cluster with a container dedicated as a master node. This node schedules the workload of all the remaining containers in the cluster. Kubernetes has become popular due to its features like container scheduling, auto-scaling, networking, monitoring, and many others.

Pros of Kubernetes:

  1. Application Availability: One of the biggest benefits of Kubernetes is that it safeguards your application from a single point of failure. Here, you can create several control plane nodes along with master nodes. If any master node fails, the other nodes will ensure that the cluster is always running. 
  2. Flexibility: Kubernetes requires a container runtime to perform. However, you do not need any specific software component to work with Kubernetes. Kubernetes is highly flexible as it can function with any container runtime you want to work with. Apart from that, infrastructure is also not an issue here as it can work with private and public clouds.  
  3. Multi-Cloud Capability: Kubernetes is not limited to just one cloud but comes with multiple cloud capabilities. With Kubernetes, you can host workloads on a single cloud or segregate them across several clouds. You can also scale the environment to different clouds.

Docker vs. Kubernetes: Differences

Having similar functionalities may not necessarily mean that both Docker and Kubernetes are alike. Rather, there are plenty of differences between the two that should be understood before you pick a container technology. With that in mind, here is a detailed comparison between the two so that you can understand more about the differences between Docker and Kubernetes.

  • Setup and Installation: The first thing towards using any container management system is its setup and installation. Kubernetes needs several manual steps for setting up Kubernetes Master and worker node components. However, that is not the case with Docker, as its installation can be done by adding a one-liner command on a Linux platform. 
  • Purpose: Docker’s primary feature is automating the application deployment in lightweight containers to ensure they can perform and generate the best outcome in different environments. On the other hand, Kubernetes is mainly used to maintain and deploy a group of containers in the private, cloud, and public environments.
  • Architecture: Docker uses a cluster of Docker hosts, which is used to deploy services. This cluster is Docker’s native solution called Docker Swarm. It clusters multiple hosts and puts the standard Docker API over that cluster, making integration with tools easier. Kubernetes follows a client-server architecture to accomplish its tasks. However, it does not come with all the major functionalities out of the box. Rather, custom plugins should be used with Kubernetes to enhance its overall features.
  • Supported Platforms for Installation: Docker supports both macOS and Windows for installing a single-node Docker swarm. The support for these leading platforms ensures that the majority of the users can deploy Docker on their systems. Kubernetes can be installed on numerous platforms, including a virtual machine on a cloud, personal computer, or just the servers. Even though it does support Windows server, it is still under beta, and the final release is not out yet.
  • Logging: Docker v17.05 and higher comes with logging driver plugins, which help determine where and how their log messages should be sent. This plugin makes the entire logging easier. Furthermore, it also comes with several different logging mechanisms to attain the right information from running services and containers. 
    Until any manual configuration is done, every Docker daemon will have a default logging driver. Apart from that, users also have the facility to use additional logging plugins. However, there is no storage solution for log data in Kubernetes.
    You must integrate other logging solutions into the cluster to achieve this functionality. Tools like Fluentd, Logz.io, and GCP can be used for this task.
  • Documentation: There are stances when you may be stuck in a situation or want to know more about the technology. In that case, its official documentation will help you get what you are looking for. Docker comes with extensive and effective documentation that will help you know all about the technology. Be it installation or it’s working, you can learn about it through its official documentation.
    Even though Kubernetes also comes with official documentation, it is far inferior compared to Docker. It only covers basic information and not all the different phases of using Kubernetes deployment.
  • Load Balancing: Load balancing is an essential feature to distribute traffic on several servers and containers. Both Docker and Kubernetes allow users to perform load balancing. However, Docker offers auto-load balancing, making it easier for the developers to accomplish this task.
    On the other hand, load balancing settings should be configured manually in Kubernetes, so the users have to make additional efforts. 
  • Scalability: When it comes to Docker’s scalability, it is surely a lot faster than Kubernetes. However, the cluster strength after scaling is not as sturdy as Kubernetes. On the contrary, Kubernetes’ scalability is slow compared to Docker’s, but the cluster state is guaranteed to be robust.
  • Updates and Rollbacks: Both Docker and Kubernetes support rolling updates. However, if any failure occurs in Docker, then its rollback should be done manually as it does not support auto rollback. Kubernetes has an upper edge over Docker here as it can auto roll back to the previous deployment in case of a failure.
  • Support: Both Docker and Kubernetes are open-source tools with active community support. Docker’s user base helps update the software and its user with the latest features and helps them rectify any issue they face.
    Kubernetes also has strong support from the community. However, it has the edge over Docker as Kubernetes has support from major organizations like Microsoft, Amazon, and IBM.

Can Docker be used without Kubernetes?

Docker is mainly used to create and manage container images and put them into operations. However, many people wonder whether it is necessary to use Kubernetes with Docker. The answer is no, it is not necessary. Docker can create and organize container images, allowing the user to put them in a multi-container application using Docker Compose. 

Docker is capable of performing almost all the major container management tasks independently. Kubernetes is used with Docker because it ensures high availability by deploying the Docker containers automatically across IT environments. Furthermore, Kubernetes lets Docker have automatic rollouts and rollbacks. 

With that in mind, it can be said that Docker can be used without Kubernetes, but using it with Kubernetes can provide additional features that can be helpful in container management.

Can Kubernetes be used without Docker?

Kubernetes is a container orchestrator that cannot build or manage container images on its own. It always requires a container runtime tool to fulfil these tasks. Docker comes with a container runtime called Docker Engine which can help Kubernetes. 

Even though using Docker with Kubernetes is a common practice, it does not mean that Kubernetes cannot function without Docker. Though it does need a container runtime, it doesn’t always need to be Docker. You can use any other container runtime with Kubernetes to get the job done. 

In other words, Kubernetes can be used with Docker, but it is not mandatory to use the same container runtime. The same tasks can be achieved by using another container runtime as well.

Conclusion:

Even though both Docker and Kubernetes work for automation of deployment and management of container-based applications, they still have several differences between them. Docker is about creating and managing containers and minimizing the time between writing and deploying the code. On the other hand, Kubernetes is preferred when the developer needs to work on a huge amount of containers on different systems. 

Whether it is Kubernetes or Docker, professional assistance will always help get the desired outcome. ThinkSys Inc. will provide you with the best container management services, which will help you deploy, run, and manage your Kubernetes clusters and Docker containers. The team at ThinkSys Inc. is highly trained in handling the entire DevOps culture for an organization.

FAQ:

Docker can create and manage containers independently. Kubernetes expand the functionalities of Docker by adding automation and the capability of handling monolithic architectures. With that in mind, it can be said that Kubernetes is not replacing Docker, but enhancing its features.

The functionalities of Kubernetes revolve around containers. Docker technology is used to create containers that will be used in Kubernetes. Considering that fact, if you wish to use Kubernetes and Docker, you need to learn Docker before you can begin working with Kubernetes.

Docker is used for building and managing containers whereas Kubernetes is more about container orchestration and deploying applications. Both these tools work together for scaling applications on the cloud.

Even though Docker swarm has a simpler structure and is easier to use, it is still not as popular as Kubernetes. The reason behind this is that the automation capabilities of Kubernetes are superior to that of the Docker swarm. Apart from that, scaling should be done manually in the Docker swarm. 

Docker containers and virtual machines are often compared with each other. However, certain differences between the two should always be considered before choosing one.

The first difference is their weight. A Docker container is lightweight as compared to a virtual machine.

Regarding resource usage, the Docker container can share resources with each other when they are used in the same operating system infrastructure. On the other hand, every virtual machine will have a separate operating system. 

As there is no sharing of OS, virtual machines are considered safer than Docker Kubernetes. However, container safety can be enhanced by using the best Docker practices. 

Building a Docker container requires a set of instructions or templates. A Docker image is the file that behaves like this template or instructions, which is used to execute a code in the container. These images are the initial step toward working with Docker. A Docker image is always in a read-only format and comes with multiple layers where each layer originates from the previous layer. 

Latest Posts:

best devops strategies

Best DevOps Strategies for Successful Outcome

The past decade has seen exponential growth in several sectors, but IT is the one with the most significant and rapid growth. As shifting to digital mediums is among the most widespread practices globally, quick software delivery and cloud computing have become challenging for organizations. DevOps has become the most reliable solution to fulfilling these goals effectively. 

With legions of enterprises shifting towards DevOps, there need to be certain strategies to be implemented to get the best results. This article elaborates on the best DevOps strategies for different actions, including DevOps deployment, release, pipeline, enterprise, and many others.

Devops best practices

What is DevOps?

DevOps promotes collaboration between the development and operations team to reduce the software development lifecycle and deliver software quickly. It combines different practices, tools, and philosophies to boost software delivery. Continuous integration and deployment are the key factors of DevOps culture, which help build applications faster and iteratively. With Agile, system theory, and lean practices as its fundamentals, DevOps is all about the incremental development of applications.

#1:DevOps Branching and Merging Strategies:

A software development team builds a branching strategy to interact with the version control system to manage or write the code. With an effective DevOps branching strategy, the team can create an efficient DevOps process that ensures the utmost end-product quality. As the gist of a branching strategy is mentioned, let us discuss some of the best strategies.

  1. GitHub Flow: GitHub flow is one of the most widely used branching strategies at GitHub, which is all about following certain rules. In this strategy, a new branch should be created off the control branch and given a name that describes it appropriately before beginning work on a new feature or a bug. Once the actual work begins, commits should be added to the branch. Once the branch is ready, a pull request should be opened. Another professional from the team should also review the changes in the branch, it can then be merged to the controller branch and can be deployed to production.
  2. The Forking Workflow: The forking workflow is a popular branching strategy among professionals who contribute to open-source projects. The developer will have a local and a server-side repository in this strategy. The developer can push to their server-side repository, whereas the maintainer can do the same to the official repository. With this action, developer contributions can be integrated into the repository without a single central repository. The maintainer can accept the developer commits without providing access to the official repository. 
    Rather than using the official repository, the developers would fork the repository to create its copy on the server, which will act as a personal public repository. Afterward, the developer can execute a Git clone to create a local copy of the online copy of the original repository. 
  3. GitFlow: GitFlow is a heavyweight process or branching strategy which depends on short-lived and two long-lived branches. The permanent branches as controller and development showcases the last good version in production and an unstable version where the developer happens, respectively. Here, different supporting branches can be used among feature, hotfix, and release branches.
    Here, the developers can push the commit to their repository rather than publishing a local commit to the original repository. Doing so assists the project maintainer in knowing about an upcoming update as the developers submit their pull requests. 
    • Feature Branches: Developers create feature branches to work on new features and should always be branched off development. The developers must merge the branch to the controller upon completing the feature.
    • Release Branches: As the name suggests, this branch is about the preparation of releases. Furthermore, the developers can prepare metadata and fix minor bugs in this branch. Being a separate branch, the development branch can still be used to attain features for the next release. Akin to the feature branch, this one should also be merged into the controller once it becomes stable for release.
    • Hotfix Branches: Hotfix branches are also meant for release in production, but they are unplanned and are done to fix a crucial bug. They are primarily used to ensure uninterrupted work on new features while fixing the bug. These branches are created from the controller and should be merged into the controller as well as to develop after the fix is complete.

#2:DevOps Deployment Strategies:

  1. Blue-Green Deployment Strategy: One of the most vastly used deployment strategies is the blue-green or red-black deployment. The new and old application versions run simultaneously using a load balancer. The idea is to implement the new version to the audience while keeping the old version as a backup.
    Whenever there is instability in the new version, or it faces downtime, the load balancer can instantly switch to the previous version, avoiding any issues for the users. The stable version is considered blue, whereas the new version is called green.
  2. Canary Deployment: In this strategy, rather than deploying the new version of the application to the entire audience, the development team uses a load balancer to target a small number of users. 
    Once these users have used the newer version for some time, the metrics will be collected from them, which will be used to eradicate bugs or make the version better for all the users. In other words, a small user base is targeted while deploying a new version to determine whether or not the program is ready for the masses. 
    With this deployment strategy, the teams can reduce the risk of introducing a program with bugs to the audience.
  3. A/B Testing Deployment Strategy: The A/B testing deployment strategy is about attaining users’ statistical data and determining whether the new version should be rolled out or rolled back. Here, a certain number of users will use the new version under specific conditions, and data should be obtained from their usage.
    Afterward, the same data is compared with the average of the previous version, and the right decision on rollout or rollback is taken. This strategy can be combined with the Canary strategy for improved results.
  4. Recreate Deployment Strategy: The recreated deployment strategy is preferred by applications with limited infrastructure and where downtime is not an issue. In this strategy, the older version is entirely shut down before deploying the new version, and a full reboot cycle is executed. This deployment strategy does not use a load balancer and is appropriate for the staging environment. However, downtime can be a major issue for certain applications.

#3:DevOps Monitoring Strategies:

  1. Understand What to Monitor: When it comes to monitoring DevOps, you should be aware of what you want to monitor in the first place. Categories like user activity, server health, vulnerabilities, application log output, and development milestones should be covered while monitoring. However, it all varies with the project and the organization.
    The rule of thumb is to monitor at least one of the primary categories to analyze the accurate efficiency of DevOps.
  2. Monitoring Functionalities: Monitoring tools can attain performance insights or data in scalable databases and track the machine learning application for reporting. There are certain monitoring functionalities that tools can provide including data collector, reports, dashboards, REST API, machine learning, diagnostics, and notifications, among others.
  3. Monitoring Tools Evaluation: Monitoring tools like Consul, God, or Collectl are highly effective in DevOps monitoring. However, it is essential to understand these tools and the functionalities they provide that will help in the evaluation process for DevOps workflow. 
    For evaluation, start by creating an outline framework for the DevOps teams. Narrow down the evaluation process by defining the goals that should be applied to the DevOps monitoring strategy. Combine the evaluation with the monitoring functionalities from the tools to make the right decision during the monitoring.

#4:DevOps Testing Strategies:

  1. Automate Tests: When it comes to one of the best testing strategies for DevOps, automation surely holds a high spot. Continuous testing is a significant part of DevOps, and it may become a challenge for the DevOps team when the pipelines are updated frequently through continuous integration.
    In that case, automating the tests as much as possible is the strategy used by major DevOps teams. Test automation can reduce several risk factors and allows the team to attain feedback quickly. Furthermore, automating tests can help teams effectively test app quality and new code iteration. Automation minimizes human intervention, so the probability of errors is minimized.
  2. Test Automation Suite: Automation within and outside CI is one of the core elements of DevOps. A stable test automation suite is necessary to ensure that automation is effective and helps achieve the desired goals. The test automation suite should be audited, reviewed, and modified whenever necessary. Whenever there is a code change, the testing should also change to ensure that it remains valid and effective.
  3. Reporting and Analysis: Testing activities rely heavily on test reports which are analyzed to determine the future course of action. With that in mind, the DevOps testing expert recommends having advanced reporting and analysis. A detailed report will allow the developers to explore the failure cause and fix the same quickly. The strategy is to use a reliable reporting platform to help in all the testing activities.
  4. Include the Team: Let’s accept that every part of DevOps is about embracing collaboration between different teams and within the team. The biggest strategy for DevOps testing is to ensure that the entire team is involved in the process. The idea is to have different brilliant brains working on DevOps testing. Remember that every other strategy may not work effectively if the entire team is not involved in testing.

#5:Enterprise DevOps Strategies:

  1. Effective Communication: As DevOps is all about collaboration between different teams, effective communication is integral to enterprise DevOps. Using communication and collaboration tools can bring two teams on the same page. With improved communication, the teams can work effectively on solving issues, making it easier for the development teams to implement and deploy changes.
  2. Initiate on a Small Scale: Enterprise DevOps is all about bringing cultural changes to an enterprise. Sometimes, it takes several years to adapt to the cultural shift. With that in mind, the best strategy is to start on a small scale rather than going all in one. Monitor the results attained from the small implementation, and if the results are satisfactory, you can move forward to implement the same to other teams on a larger scale.
  3. Only fix the Existing Issues: Sometimes, enterprises ditch their preferable practices to implement widely popular practices. However, they forget that they are replacing something that has provided proven results in the past. Rather than replacing those practices or fixing something that works perfectly, the ideal strategy is to focus on those things that give positive outcomes.
  4. Seek Professional Help: Running an enterprise requires sheer determination and hard work. DevOps requires professional experience, which may not be present in new organizations. In that case, either full-time professionals or seeking assistance from DevOps outsourcing organizations is the strategy that will always provide the best results. They will have a deep understanding of DevOps. Furthermore, there is a high chance that they have faced certain challenges previously, which will help them make your enterprise DevOps better.

#6:DevOps Release Management Strategies:

  1. Aim for Minimum User Impact: The goal of every release manager is to eradicate testing and regression before a release to minimize user impact. Downtime can be reduced by active monitoring, hands-on testing, and collaborative efforts to diagnose issues while releasing. With this strategy, the DevOps team can identify issues before the users notice them. All these actions will ensure the least user impact of a release.
  2. Immutable Programming: Any object whose state cannot be changed post its generation is an immutable object. A common release strategy is to deploy an all-inclusive image with the configuration instead of modifying the configuration of an existing machine. This strategy avoids unexpected bugs in the release, making the release consistent and users encouraged while using the application.
  3.  Streamline CI/CD Pipeline with Shift Left: Often, developers move testing, QA, and automation in the early stages of SDLC, allowing them to diagnose underlying issues. This practice is also called the shift-left strategy. This strategy aims to decrease the feedback time and maintain a reliable CI/CD pipeline.
  4. Automate as much as Possible: One of the key elements of DevOps release management is automation. The goal should be to automate all the possible tasks so that the team’s efficiency can be increased. Automation will not only reduce the possibility of human errors but will also give more time to your team members to focus on enhancing the DevOps release strategy.

Conclusion:

Understanding the key DevOps strategies is surely a positive step towards making DevOps efficient. However, it may not be sufficient for larger enterprises due to vast areas that require constant attention. The solution is connecting with a DevOps organization that can help implement and manage the entire DevOps culture. 

ThinkSys Inc. is a pioneer in the DevOps industry with over a decade of experience. The professionals at ThinkSys will study your current infrastructure and create an extensive strategy that will suit your organization’s goals. The right strategy will allow your organization to efficiently attain all the benefits of DevOps.

FAQ:

Though a Docker container can run multiple processes, running a single application in a container is advisable. Horizontal scaling can be made more accessible when the applications are split into multiple containers.

 

Docker allows the user to store data in the images. However, this is not the proper practice as it not can lead to data loss or reduce data security. Instead, it is advised to store data directly on the host.

Latest Posts:

Docker Best Practices 2022

Docker Best Practices 2022

When it comes to packaging and delivering applications, legions of organizations are adopting Docker, especially for cloud-based applications. With benefits like caching a cluster of containers, scalability, quick deployment with any dependencies, and scalability, Docker has become the primary preference of many organizations. However, these benefits can be utilized when the best practices are implemented. With that in mind, this article elaborates on all the docker best practices that will help get the best results (from Docker); be it enhancing its security or effectiveness. 

Docker is a widely used platform that helps develop and run applications quickly. Moreover, it helps in managing the infrastructure akin to managing the applications. It helps deliver applications quickly by allowing the user to separate them from the infrastructure. Docker’s testing and deploying methodologies can help diminish the overall delay between code writing and production running. 

Docker Best Practices 2022

Now that a brief about Docker is mentioned, it is time to dig deeper into the primary focus: the best practices of Docker. To make things more understandable, all the Docker practices are segregated into different categories depending on their functionality. 

#1: Docker Image Building Best Practices:- 

  1. Version Docker Images:A common practice among Docker users is using the latest tag for images which is also the default tag for images. Using this tag will eradicate the possibility of identifying the running version code based on the image tag. Not only does it become easier to overwrite it, but it also leads to extreme complications while doing rollbacks. Make sure to avoid using the latest tag, especially for base images, as it could unintentionally lead to the deployment of a new version. Rather than using the default tag, the best practice is to use descriptors like semantic version, timestamps, or Docker image IDs as a tag. With the practice of having a relevant tagging scheme, it becomes easier to tie the tag to the code. 
  2. Avoid Storing Secrets in Images: Undeniably, confidential data or secrets like SSH keys, passwords, and TLS certificates are highly sensitive for an organization. Storing such data in images without encryption can make it easier for anyone to extract and exploit it. This situation is extremely common when images are pushed into a public registry. Rather than that, injecting these through build-time arguments, environment variables, and an orchestration tool is the best practice. In addition, sensitive data can also be added to the .dockerignore file. Another practice to accomplish this goal is by being specific about the files that should be copied over the image.
    Environment Variables:Environment variables are primarily used to keep the application flexible and secure. These can also be used to pass on sensitive information or secrets. However, these will still be visible in the logs, child processes, linked containers, and docker inspect. The following is a frequently used approach for managing secrets.

    $ docker run --detach --env "DATABASE_PASSWORD=SuperSecretSauce" python:3.9-slim
    
    d92cf5cf870eb0fdbf03c666e7fcf18f9664314b79ad58bc7618ea3445e39239
    
    $ docker inspect --format='{{range .Config.Env}}{{println .}}{{end}}' d92cf5cf870eb0fdbf03c666e7fcf18f9664314b79ad58bc7618ea3445e39239
    
    DATABASE_PASSWORD=SuperSecretSauce
    
    PATH=/usr/local/bin:/usr/local/sbin:/usr/local/bin:/usr/sbin:/usr/bin:/sbin:/bin
    
    LANG=C.UTF-8
    
    GPG_KEY=E3FF2839C048B25C084DEBE9B26995E310250568
    
    PYTHON_VERSION=3.9.7
    
    PYTHON_PIP_VERSION=21.2.4
    
    PYTHON_SETUPTOOLS_VERSION=57.5.0
    
    PYTHON_GET_PIP_URL=https://github.com/pypa/get-pip/raw/c20b0cfd643cd4a19246ccf204e2997af70f6b21/public/get-pip.py
    
    PYTHON_GET_PIP_SHA256=fa6f3fb93cce234cd4e8dd2beb54a51ab9c247653b52855a48dd44e6b21ff28b
    

    If the motive is to keep the secrets slightly secure, this is the way. However, it does not offer the utmost security. In case the secrets are to be shared in a shared volume, the best practice is to encrypt the secrets. 

  3. Using a .dockerignore File: The .dockerignore file is used to define the required build context. Before an image is built, the user has to specify the files and folders that should be excluded from the initial build context sent to the Docker daemon which is done with the help of the .dockerignore file.  Prior to evaluating the COPY or ADD commands, the entire project’s root is sent to the Docker daemon, making it a hefty deal. Apart from that, there can be stances when the daemon and the Docker CLI are on different machines. In that case, the .dockerignore file should be added to local secrets, temporary files, local development files, or build logs. Doing so can boost the build process, avoid secret leaks, and reduce the Docker image size. 
  4. Image Linting and Scanning: Inspecting the source code for any stylistic or programmatic error that can cause issues is called lining. Linting can help ensure that the Dockerfiles comply with the right practice and can be maintained. This process can also be followed in images to determine any underlying vulnerabilities or issues. 
  5. Signing and Verifying Images: Sometimes, tampering can be done by man-in-the-middle attacks on the images used to run the production code. By using Docker Content Trust, you can sign and verify the images, allowing you to determine whether the Docker images have been tampered with. All you have to do is set up the DOCKER_CONTENT_TRUST=1 environment variable. 

If an image is pulled and has not been signed, the following error will pop up.

Error: remote trust data does not exist for docker.io/namespace/unsigned-image:

notary.docker.io does not have trust data for docker.io/namespace/unsigned-image

#2: Dockerfiles Best Practices:

  1. Multi-Stage Builds: Dockerfiles can be divided into numerous stages through Multi-stage builds. With this break-up, the final stage is when the image is created so the tool and dependencies of application building can be discarded. In addition, multi-stage builds will lead to a modular, lean, lighter, and secure image, saving time and money.  
  2. Appropriate Docker File Command Order: The Dockerfile commands play a crucial role in its efficiency. To enhance the builds, Docker caches every layer in a specific Dockerfile. Whenever there is a change in a step, the entire cache will become invalid for all the steps afterward. This practice is highly inefficient in a Docker container.Instead of putting files randomly, the right practice is to put the frequently changed files at the end of the Dockerfile. Apart from that, you can also put layers with a higher possibility of changes lower in the Dockerfile, and turn off caching in a Docker build whenever necessary by adding –no-cache=True flag.
  3. Small Docker Base Images: When it comes to pushing, pulling, and building images, the industry-wide practice is to ensure that the images are as small as possible. This practice is because small images can make the process quicker and safer and ensure that only the libraries and dependencies included are essential for running the application. Regarding picking the right size, here is a quick comparison of different Docker base images for Python. 

    REPOSITORY

    TAG SIZE
    Python 3.9.6-alpine3.14     45.1MB
    Python 3.9.6-slim 115MB
    Python 3.9.6-slim-buster 115MB
    Python 3.9.6 886MB
    Python 3.9.6-buster

    886MB

    It is all about finding the right balance, allowing you to have small Docker base images. 

  4. Reduce the Number of Layers: With every layer, the size of the image increases due to caching. As stated above, keeping the image size minimal is the right practice. However, increasing the number of layers may not get the job done. The number of layers can be reduced by combining related commands whenever possible. Apart from that, eradicating unwanted files in the RUN step and minimizing running apt-get upgrade can also help in this task.However, this reduction in layer numbers should not be forced as it can lead to unnecessary issues. It should be done only whenever it is possible rather than making it possible forcefully. 
  5. Use COPY Instead of ADD: Numerous users believe that both COPY and ADD commands serve the same purpose with the same nature. Even though they are used to copy files from a location to a Docker image, they have certain differences.
    COPY is used to copy local files from the Docker host to the image. However, ADD can accomplish the same task but can also download external files and unpack the contents of any compressed file in the desired location.
    With a massive difference between the two, the preferred command should be COPY instead of ADD. However, you can use ADD if you want the additional functionality of ADD.
  6. Use One Container for One Process: Even though an application stack can run multiple processes in a single container, it is always advised to run only one process per container. This practice is considered one of the best for Dockerfile because it makes the below-mentioned services easier. 
    1. Reusability: When another service requires a containerized database, the same database container can be used.
    2. Portability: As there are fewer processes to work on, making security patches becomes easier.
    3. Scalability: Services can be scaled horizontally to manage traffic when there is a specific container.
  7. HEALTHCHECK Inclusion: An APU in Docker can provide a deeper insight into the status of the running process in the container. Not just running status, but stuck, still launching, and working status can be attained in Docker. However, the HEALTHCHECK instruction can be used to interact with the API even further. You can also set custom endpoints and configure the instruction to test the data. You can monitor the health status by the following docker inspect:docker inspect –format “{{json .State.Health }}” ab94f2ac7889{  “Status”: “healthy”,  “FailingStreak”: 0,

      “Log”: [

        {

          “Start”: “2021-09-28T15:22:57.5764644Z”,

          “End”: “2021-09-28T15:22:57.7825527Z”,

          “ExitCode”: 0,

          “Output”: “…”

#3: Docker Development Best Practices:

  1. CI/CD for Testing and Deployment: Experts recommend using Docker Hub or any other CI/CD pipeline to build and tag a Docker image whenever a pull request is created. Furthermore, the images should be signed by the development, security, and testing teams before they are pushed to production so that it is constantly tested for quality by the desired teams. 
  2. Use Different Environments for Development and Testing: One of the best practices while using Docker for development is creating different testing and development environments. Doing so will allow the developer to keep the Docker files isolated and execute them without influencing the final build after testing. 
  3. Update Docker to the Latest Version: Before you begin working on a Docker project, you need to ensure that you update the Docker to the latest version. Even though it will not directly impact the project, it will provide you with the latest features that Docker has to offer. New updates also have certain security features, safeguarding the project from potential attacks. 

#4: Docker Container Best Practices:

  1. Frequently Backup a Single Manager Node: A common Docker container practice is to back a single managed node frequently, which helps admins in restoration. Docker Swarm and Universal Control Plane data are part of every node, so backing up a single manager node can get the job done for the admins.
  2. Cloud Deployment of a Docker Container: When deploying a Docker container to a cloud, both Amazon Web Services and Microsoft Azure do not have integrated hosts optimized for Docker. They use the Kubernetes cluster for deployment. A standard virtual machine should be created by the admins who prefer to deploy a single container. Apart from that, securing the secure socket shell and installing Docker is the next step. Admins can now deploy the application on a cloud after installing Docker. 
  3. Control Docker Containers through a Load Balancer: A load balancer helps admins get good control over Docker containers which helps them in making containers highly available and scalable. The most commonly used load balancer is NGINX which can easily be installed on Docker. This load balancer supports multiple balancing methods, static and dynamic caching, rate limiting, and multiple distinct applications. 

#5: Docker Security Best Practices:

  1. APIs and Network Configuration: One of the biggest security threats is an inappropriately configured API which can be the target point of hackers. Make sure to configure the API securely in a way that it does not make containers publicly exposed. Practice like certificate-based authentication is an excellent way to start this task. 
  2. Limit Container Capabilities: Docker comes with a default configuration for containers where they can have capabilities that may not be required for them to perform their services. These unnecessary privileges can be a gateway for security breaches. The best practice to avoid such security vulnerabilities is to limit the container capabilities to only those which are required by the container to run applications. 
  3. Restrict System Resource Usage: Each container can use infrastructure resources like CPU, memory, and network bandwidth. Limiting the usage for each container ensures that no container uses excessive resources than required so that the services are not disrupted. Moreover, the resources will be used efficiently. 
  4. Use Trusted Images: Using images from any source, including untrusted ones, can weaken the Docker container’s security. Make sure to get Docker base images from trusted sources only. Also, the images should be configured correctly and signed by enabling the Docker Content Trust.
  5. Least Privileged User: Docker containers come with root privileges by default, providing them admin access to the container and the host. This access can make container security vulnerable and easier for hackers to exploit Docker. Setting a least-privileged user will provide only the required privileges to run containers, ultimately eradicating the aforementioned issue and improving Docker security. 
  6. Limit Access to Container Files: Transient container files are accessed frequently as they need constant bug fixes and upgrades which exposes them drastically. This issue can be solved when the container logs are maintained outside the container, as it will minimize the usage of container files. Moreover, the team will not access logs for fixing underlying issues in the container. 

#6: Docker Logging Best Practices:

  1. Logging from Application: Logging directly from the application is a method where applications within the container manage the logging through a framework. The developers will have the utmost control over the logging event when using this method. Furthermore, the applications remain independent from containers as well.
  2. Logging Drivers: Logging drivers is a unique feature of Docker that helps read data by the stderr or stdout streams of the container as they are specifically configured to accomplish this task. Once done, the host machine stores log files that include the prior data. Logging drivers are used because they are native to Docker and centralize the log to a single location. 
  3. Dedicated Container for Logging: Having a dedicated container for logging helps in eradicating dependencies on host machines. This container will be responsible for log file management within the Docker environment. This dedicated logging container will cumulate logs from other containers and monitor and analyze them automatically. Furthermore, it can forward the log files to a central location. Another excellent thing about this practice is that you can deploy more containers whenever you require.
  4. Sidecar Method: The Sidecar method is undoubtedly among the best if you want to manage microservices architecture. Here, the sidecars run simultaneously with the parent application, where they share the same network and volume. These shared resources allow you to expand the app functionalities and eradicate the need to install any extra configurations. 

#7: Docker Compose Best Practices:

  1. Adjust Compose file for Production: Sometimes, making certain changes like binding different ports on the host, enhancing extra services, different setup for environment variables, and eradicating volume bindings are necessary to prepare for production. To accomplish this task, the best practice is to define a new Compose file that will specify the desired configuration. In this configuration file, you are only required to add changes you want from the original Compose file. You can apply the new Compose file over docker-compose.yml for a new configuration. You can guide Compose to use the second configuration file with the –f option. 
  2. Deploy Changes: Rebuilding the image and recreating application containers is necessary whenever a change is implemented to an app code. The below-mentioned code can be used to redeploy web service. 
    $ docker-compose build web
    $ docker-compose up –no-deps -d webWith this code, the web image will be rebuilt and stopped. Afterward, it will destroy and recreate the web service.  The inclusion of –no-deps flag avoids the creation of any services by Compose used by the web.
  3. Run Compose on a Single Server: Compose can be used to deploy an application to a remote Docker after setting up DOCKER_CERT_PATH, DOCKER_HOST, and DOCKER_TLS_VERIFY environment variables. After setting up these variables, the docker-compose commands will no longer require any additional configuration and perform as desired.

Docker Services Offered by ThinkSys:

  • Docker Implementation: ThinkSys Inc. provides an industry-leading Docker implementation service where our professionals will understand the requirements of your organization and create a roadmap for its implementation. Our implementation services include configuration of Docker in your IT infrastructure and integration of Docker with other applications.
  • Docker Container Management: Our Docker container management services start by analyzing the containers and identifying any underlying issues. Our experts will manage the containers in this service to ensure they perform effectively and efficiently. Furthermore, we will try to optimize the Docker environment while keeping it as secure as possible.
  • Docker Consulting Service: Whether you want to implement Docker containers or just want to know now about Dockers, our docker experts. can help you with Docker consulting services. With our services, you can upgrade to a microservice-based architecture.  
  • Docker Support: Bugs and issues can occur during or after Docker implementation. Our Docker experts can provide you with around-the-clock Docker support to ensure that your Docker containers remain functional. With our experienced professionals by your side, you are sure to get top-notch Docker support whenever you want. 
  • Docker Customization: Do you want to customize your Docker containers? ThinkSys Inc. can help you personalize your Docker through custom plugins and API. These plugins can be modified as per your organization’s needs as well.
  • Docker Security: Whether you want to enhance the security of your existing Dockerized environment or make sure that your new Docker remains secure, all you have to do is connect with ThinkSys Docker Specialists. We will use the best Docker security practices to ensure that your Docker environment remains highly secure and meets all the desired security standards.
  • Proof of Concept: Sometimes, you want to accomplish a task in your Docker but remain unsure whether it will be the right decision. ThinkSys Inc. will analyze your Docker containers and the new task you want to complete. Based on that study, you will be provided with a report on how accomplishing this complex task will influence your organization and whether it is worth it or not.
  • Container Management: ThinkSys Inc. can also assist you in the management of your containers for mobile and web-based applications that use Kubernetes. Depending on your organization’s requirements, you will get automatic container scaling, deployment, and creation. 

Connect with ThinkSys Inc Docker Experts Today

Conclusion:

Docker is one of the most widely used software platforms for software building, testing, and deployment. With tons of features, there is also a possibility of additional complexities. The Docker practices mentioned above will not just reduce the complexities in Docker but will ensure that you get the best outcome from this software platform. 

Sometimes professional assistance is required for using Docker. With over a decade of experience, ThinkSys can provide the best docker assistance you need. The team at ThinkSys is equipped with the industry-leading tools and practices that will help you get the best outcome from Docker.

Connect with ThinkSys for the best Docker or any other software development services. 

Though a Docker container can run multiple processes, running a single application in a container is advisable. Horizontal scaling can be made more accessible when the applications are split into multiple containers.

 

Docker allows the user to store data in the images. However, this is not the proper practice as it not can lead to data loss or reduce data security. Instead, it is advised to store data directly on the host.

 

Kubernates MultiTenancy

Understanding Multi-Tenancy in Kubernetes

A container orchestration system is one of the most widely used automating software scaling, deployment, and management. Created by Google and managed by Cloud Native Computing Foundation, Kubernetes is an open-source container orchestration system used by legions of organizations. Container orchestration helps in operational tasks like networking, provisioning, and deploying in containerized workloads. Several organizations can use multiple workloads in a single Kubernetes cluster that shares the same infrastructure. This strategy used by enterprises is called multi-tenancy. This article is all about understanding multi-tenancy in Kubernetes, including its use cases, best practices, and how it helps organizations in the cloud-native space.

What is a Multi-Tenancy in Kubernetes?

Tenants are separate entities in an organization but share several shared components like infrastructure. Every tenant in an organization is given some shared components along with some isolation. Achieving this isolation can be done in multiple ways, like giving every tenant a unique server or virtual machine. Even though it is effective, the efficiency is compromised here as it will be costly and will not use the resources the right way. This is the part where multi-tenancy in Kubernetes comes into play. 

Kubernetes Multi Tenancy

Multi-tenancy is the capability to run different entities’ workloads in a single cluster shared by different tenants. The organizations running multi-tenant clusters have to isolate each tenant to avoid any potential damage caused by a malicious tenant. In simpler terms, multi-tenancy is allocating an isolated space to an entity in a cluster while also giving some shared cluster components. This model is used in organizations running several applications in a single environment or where different teams exist but share a single Kubernetes environment.

Types of Multi-Tenancy in Kubernetes

  • Soft Multi-Tenancy : Soft multi-tenancy is mainly done when numerous projects or departments are running in an organization or when there are trusted tenants. It can be implemented through the Kubernetes namespace multi-tenancy. In this type of multi-tenancy, the Kubernetes cluster is separated among different users but without extreme or strict isolation. The primary reason why this type of multi-tenancy is implemented in the Kubernetes cluster is to assist with resource separation and avoid any accidental access to resources. As isolation is not strict in this type, deliberate attacks by one tenant on another cannot be prevented or minimized. This type is only preferred for trusted tenants in a Kubernetes environment with that thing in mind. 
  • Hard Multi-Tenancy : Organizations having legions of tenants in a single Kubernetes cluster may have both trusted and untrusted tenants. Implementing hard multi-tenancy in Kubernetes applies much stricter isolation than soft mule-tenancy, hindering tenants from influencing each other. Even the malicious tenants cannot affect any other tenants in the cluster. As this multi-tenant type comes with stricter isolation, enforcing it is also more complicated. The virtual cluster and namespace configuration is the tricky part of its implementation. 

Single Tenancy VS Multi-Tenancy(How Multi Tenancy is different from Single tenancy?)

Both single and multi-tenancy are different from each other in many ways. Before you make your next move, it is crucial to understand the key differences each tenancy type will bring. 

  • Cost : Every tenant will have a unique cluster with a control plane, master nodes, monitoring, and other management components in a single tenancy. Implementing and managing a separate cluster with management components for each tenant is highly expensive, especially in a large organization with tons of tenants. On the other hand, multi-tenancy gives an isolated space to each tenant but on a single cluster where the organization can reuse resources, making it a cost-effective option. 
  • Complexity: Having a single cluster for each tenant is not just time-consuming but also complex. Even though the process can be automated through managed services, still it should be done for each cluster. A multi-tenancy method is less complicated as there is a need for setting up only one cluster for multiple tenants. 
  • Security: As there is a single cluster for every tenant, their isolation is natural; hence, none of the malicious tenants can affect any other. Due to this reason, a single tenancy is preferred for untrusted tenants. However, Kubernetes multi-tenancy security management can be a tussle as tenants share a single cluster. Though the security can be enhanced with Role-Based Access Control and hard multi-tenancy, still it is extra work that requires additional time and effort.

Multi-Tenancy Models in Kubernetes:

Kubernetes multi-tenancy models help in making its use cases easier and uncomplicated. Depending on the organizations and teams, these models can be implemented for the best outcome. Three most commonly implemented multi-tenancy models can be applied to projects. 

  1. Namespaces-as-a-Service: In this model, every tenant shares a cluster where their workloads are restricted only to a set of namespaces allocated to the particular tenant. However, all the control plane resources, including the scheduler and AP server, CPU, and memory, are accessible by all the tenants across the cluster. The namespaces-as-a-service model allows tenants to share all the cluster resources, hindering clusters from updating or creating any of such resources. When isolating tenant workloads, their namespace should contain role bindings, resource quota, and network policies. Adding these to the namespaces is necessary as they help control access to the namespace, limit usage in the tenants, and prevent network traffic in all the tenants. 
  2. Clusters as a Service: In clusters as a service multi-tenancy model, every tenant is given a cluster where they can use cluster-wide resources. Moreover, they can have a Kubernetes control plane with complete isolation where management cluster projects are used to provision multiple workload clusters. Furthermore, the tenants are provided with a workload cluster that provides complete control of the cluster resources. The central platform teams manage add-on services like security, monitoring, upgrading, patching, and cluster lifestyle management services. However, certain limitations exist for the tenant admin to modify the services above. 
  3. Control Planes as a Service: The control planes as a service variant of the earlier mentioned CaaS model. However, a tenant may be assigned a virtual cluster where they will be given an exclusive control plane. This model is applicable when the virtual cluster’s users cannot determine the differences between a Kubernetes cluster and a virtual cluster. Even though a virtual cluster is allocated to tenants, they still have to share worker node resources and certain control plane components. This model is implemented by a virtual cluster project where several VCs share a super-cluster.

Kubernetes Multi-Tenancy Use Cases

Here are the most frequently use cases of the Kubernetes multi-tenancy models:

  1. SaaS Provider Multi-Tenancy: The Software-as-a-Service control plane and the customer instance are the tenants of a SaaS provider’s cluster. Every application instance is organized with its namespaces and the SaaS control plane components to take full leverage of the namespace policies. Every end-user has to use the interface provided by SaaS, which communicates with the Kubernetes control plane. This process has to be followed by every user as they cannot communicate with the control plane directly. The biggest example of SaaS provider multi-tenancy is a blogging platform running on a multi-tenant cluster. Here, the platform gives them a control plane, and their user’s blog will have a separate namespace. The users can use all the services through its interface without viewing the operation of the cluster. 
  2. Enterprise Multi-Tenancy: When it comes to tenants in an enterprise, they are mainly different teams of the same organization that comes with a namespace. However, managing these tenants per cluster in an alternative multi-tenancy model is complicated. Furthermore, the network traffic between tenants should be defined correctly. This task in multi-tenancy can be accomplished through Kubernetes network policy where the cluster users can be categorized into cluster-admin, namespace admin, and developer. 
    A cluster admin will handle the cluster and its tenants with authority to create, update, delete, and read any policy object. Moreover, they can also create and assign namespaces. The next is the namespace administrator, who will handle single tenants in the namespace. The last role is of the developer who can read, delete, create, and update namespace non-policy objects. However, this role is limited because their authority lies within their accessible namespaces. 
  3. Multiple Applications on a Single Cluster: There are stances when organizations want to host multiple applications on a single cluster. This need can be fulfilled through multi-tenancy, where you can host several related and unrelated applications that require a scalable platform but in a single cluster. 
  4. Hosting Trusted and Untrusted Tenants: An organization has to work with both trusted and untrusted tenants that may be malicious. Without a doubt, the organization never wants to compromise tenants’ security. This is the part where the multi-tenancy cluster comes into play. Here, the organization can share the infrastructure with both types of tenants without worrying about security. They can host apps needed by the internal teams along with the external entities that may require access to your cluster for workloads.

Best Practices for Kubernetes Multi-Tenancy 

Kubernetes multi-tenancy can be used for many different use cases. However, the right practices must be followed to get the most out of it. With that in mind, here are some of the best practices for Kubernetes multi-tenancy. 

  • Cluster Personas: Creating a hierarchy of personas will maintain transparency within the process and avoid clashes in the team. Considering that fact, it is best to create a hierarchy of cluster personas based on the actions they can perform and the permissions they require to accomplish their tasks. There are four different personas in a multi-tenancy environment; cluster view, cluster-admin, tenant admin, and tenant user. 
  • Role-Based Access Control (RBAC): No matter from where the request originates, every create, read, delete, and update operation is done through the Kubernetes API server in multi-tenancy. When there are multiple tenants in a cluster, it is essential to keep it as secure as possible. Enabling Kubernetes multi-tenancy RBAC in an API server will help get better control over the applications and users in the cluster. Every RBAC has four API objects: Role, RoleBinding, ClusterRole, and ClusterRoleBinding. Furthermore, disabling attribute-based access control is also recommended. 
  • Namespace Categorization: In a multi-tenant Kubernetes environment, the namespace is among the most crucial aspects. One of the best practices regarding namespace is categorizing it into different groups. The most commonly used groups are system, service, and tenant namespace. The system namespace is exclusive for system pods, whereas the service namespace should run apps whose access is required by other namespaces in the cluster. However, a tenant namespace is a group used to run services and applications which do not need access from any other namespace.  
  • Label Namespaces: Another excellent yet underrated practice in multi-tenancy is labeling namespaces. This practice helps metadata applications to understand the reason for using resources. Labeling the namespaces will help understand the metrics whenever necessary or filter the application’s data easily. 
  • Use Network Policy: In a multi-tenant environment, it is essential to isolate tenant namespaces. This can be done by using network policies that let the cluster admins control the communication of group pods. Admins should use network policy resources to isolate tenant namespaces. 
  • Limit Shared Resource Usage: When multiple tenants exist in a cluster, they are bound to use shared resources. Sometimes, these resources can be wasted by a tenant, reducing the outcome for other tenants. A great way to eradicate this issue is by limiting the shared resource usage by implementing Kubernetes namespace resource quotas. Through this quota, you can control the total resource usage by a single tenant.  

Kubernetes Multi-Tenancy Cluster Setup

Experts recommend having a single large cluster in a multi-tenancy environment rather than having multiple small clusters for different tenants. Here is a quick guide on setting up clusters in a multi-tenancy environment. 

Step 1: Partition cluster depending on the workload using namespaces:

The first step is to set up a cluster based on the development workload requirement. The basic cluster configurations come with four nodes with a single CPU and four gigabytes of RAM. This process includes setting up two teams in a cluster with a separate namespace for each team. Though the Kubernetes cluster comes with a default namespace, new namespaces can be created for the teams. For instance, the following namespace can be created for the leadership and virtual teams.

kubectl create namespace team-leadership

kubectl create namespace team-virtual

Furthermore, creating a sample application in these namespaces is also part of this step. This process will guide you in deploying an Nginx pod in the created namespaces using the below-mentioned command.  

kubectl run app-leadership–image=nginx –namespace=team-leadership

kubectl run app-virtual –image=nginx –namespace=team-virtual

Step 2: Grant access control to the teams:

Once the namespaces and the applications are ready, it is time to provide them access control. To accomplish this task, the first thing to do is to create a service account for the team and assign the IAM role. Once that is done, you need to create a Kubernetes role with basic CRUD permissions. Moreover, you need to assign this role to the IAM service account created earlier in the process. 

Step 3: Test the Access:

Now that the roles are assigned, the right practice is to test the access. To do that, you need to download the JSON key of the service account and try to log in. After you have successfully logged in to the service account, make sure to access the app in the namespace. Follow the same process for every namespace that you have created. 

Step 4: Assign Resources to the namespace:

When there are multiple tenants or namespaces in a single cluster, they share some resources, so resource allocation is necessary. This allocation can be achieved by using ResourceQuota Kubernetes, which you will configure for namespaces regarding the resources they can utilize like total storage space, CPU, memory, pods, and services. You can restrict the resource utilization in each namespace so that none of the tenants overutilize resources. 

Step 5: Resource Utilization Monitoring:

As you have already allocated resources to each namespace in the previous step, it is time to watch resource utilization. When new use cases are added to the namespace, the resource utilization may change. With that in mind, it is always advised to understand the resource usage pattern to make sure that every namespace gets the right amount of resources as per their usage. 

What is Hypernetes in Kubernetes Multi-Tenancy?

Often, organizations run containers inside a VM to enhance its security. Undeniably, it is an effective method, but it comes with certain issues like the inability to manage container networks uniformly through the IaaS layer or the lack of centrally scheduled resources in containers. In that case, the alternative to this method is Hypernetes. A Kubernetes multi-tenant Distro adds a Hyper-based container execution engine, a container SDN network, cinder-based persistent storage, authentication, and authorization to the Kubernetes. 

Furthermore, Hypernetes adds certain components to Kubernetes like isolated tenants managed by keystone, Layer 2 isolation network for tenants, isolation of containers through virtualization-based container execution engine, and persistent storage. Apart from that, Hypernetes provides numerous components based on Kubernetes through different plugins. 

Conclusion:

Undoubtedly, using multiple clusters for each tenant is not a practical way of containerizing applications. The Kubernetes multi-tenancy has been proven to be an efficient way of storing applications. It is cost-effective but saves container setup time and a lot of resources as well. As multi-tenancy does not come out of the box in Kubernetes, organizations may require professional assistance. ThinkSys Inc can provide you with the unique strategies to implement multi-tenancy in Kubernetes that will expand its overall usability and attain efficient resource utilization. Furthermore, ThinkSys’ dedicated Kubernetes toolset ensures effective multi-tenancy implementation in a cluster.  

Chat with ThinkSys Kubernetes Experts to Implement multi-tenancy in Kubernetes

Frequently Asked Questions(Kubernetes)

A multi-tenant schema is when the application determines which schema to connect to for a tenant after connecting to a database.

A Kubernetes multi-tenancy is an architecture that helps run workloads of different entities in a single cluster but with isolation. Here, the workloads are also called tenants, which share the same cluster and its resources but are kept separate. 

Multi-tenancy is when a single cluster serves multiple tenants rather than creating a separate cluster for each tenant. Every tenant shares the cluster along with the database. However, their data is always isolated. 

The AWS supports multi-tenancy where SaaS applications can have multiple tenants with isolation. The level of isolation in Kubernetes multi-tenancy AWS and the shared resources is influenced by factors like domain nature, AWS services, and the multi-architecture model. 

Kubernetes has the ability that allows containers to run on several machines, be it physical, on-premises, virtual, or cloud. Moreover, these containers support all the major operating systems and share the same to run on multiple machines.

The most significant difference between a single tenant and a multi-tenant is that the former will provide a separate database to a customer. In contrast, a multi-tenant can serve multiple customers with a single database. Multi-tenancy is proven to be cost-effective and resource-efficient for large organizations. 

A Kubernetes multi-tenant deployment is when multiple software instances run on a single cluster. This cluster will have multiple tenants who will share the resources while being in isolation simultaneously. 

Several customers share a multi-tenant cluster called tenants. The cluster operators will isolate the tenants where they will allocate resources for each tenant depending on the requirements. 

Related DevOps Blogs:

  1. Best DevOps Automation Tools 2022.

  2. Top 15 DevOps Metrics and KPI’s.

  3. Azure DevOps Pipeline Guide 2022.

  4. DevOps On Cloud In 2022.

  5. How Microservices Comes Brilliantly With DevOps.

  6. How Offshore Development has Changes with DevOps?

  7. Application Development With Microservices In DevOps Age.

  8. Understanding the Kubernetes Architecture.

  9. Understanding Docker Components.

Docker Components

Understanding Docker Components

As the race to release software quicker than ever continues, organizations take extraordinary measures to reach the number one spot. Software development is the most significant factor that directly influences the entire software lifecycle. Docker is one of the most popular software development platforms that has revolutionized how software development works. 

Legions of software development companies use Docker to enhance the overall development process. However, certain components of Docker should be understood to get the best results. This article explains all about basic and advanced Docker components, Docker architecture, how it works, and some of the best practices

What is Docker?

Docker is an open-source virtualized software platform that helps enterprises create, deploy, and run applications in containers. A Docker container is a lightweight package that has its own dependencies, including bins and frameworks. With a Docker container, the developer can deploy the software quickly by separating them from the infrastructure. 

Docker containers have become immensely practical because they eradicate the need for a separate operating system or guest OS installed on the host operating system. A container can run using a single OS without needing to install multiple guest operating systems. 

  • Portability: Whenever you want to deploy your tested containerized application to any other system, you can rest assured that it will perform exactly the same in every other system. The only catch is that the new system should have a running Docker. It will save a lot of time that would be spent on setting up the environment and testing the application. 
  •  Simple and Fast: Docker is far-famed for simplifying the process. When deploying a code, the users have to put their configuration into the code and deploy it. The entire process is seamless and does not come with any complications. Furthermore, the infrastructure requirements are not linked with the application environment, simplifying the process even further. 
  • Isolation: One of the key benefits of Docker is the isolation it provides for the applications. Sometimes, removing the applications may leave behind temporary files on the operating system. As every Docker container has its resources isolated from other containers, each application remains separate from the others. When it comes to deleting the application, the entire container can be removed, which will have no impact on other applications and ensures that it does not leave behind any configuration or temporary files. 
  • Security: Each Docker container has its own set of resources and is segregated from other containers. This container isolation gives the developer maximum control over container management and traffic flow. As no container can look or influence the functioning of any other container, the security is never compromised, ensuring a secure application environment.  

Docker Vs. Virtual Machine:

Due to its similar functionalities, people often compare Docker with a virtual machine. Though they may seem similar initially, there are vast differences between them. With that in mind, here is a quick yet detailed comparison between a Docker and a Virtual Machine

  1. Operating System: The main difference between Docker and a VM is how they support the operating system. A Docker container can share the host operating system, making them lightweight. On the contrary, each VM has a guest OS over its host operating system, making them heavy. 
  2. Performance: Even though Docker and VMs are used for different purposes, still comparing their performance is a crucial element. The heavy architecture of a VM is the cause of its slow booting. However, the lightweight architecture combined with the capability of customized resource allocation makes Docker boot up faster. Also, Docker can work on a single operating system that eradicates any additional effort, making duplicating and scaling easier. In simpler terms, Docker is proven to be better performing than a VM. 
  3. Security: Security is the part where virtual machines have the more significant advantage. As VMs do not share any operating system and come with strict isolation in the host kernel, they remain highly secure. Unlike VMs, a Docker has a shared host operating system and may even share certain resources, posing a security threat to not just one container but all the containers sharing the OS. 

Docker Architecture:

Docker uses a client-server architecture where the Docker client communicates with the Docker daemon. The Docker daemon is responsible for creating, running, and distributing containers.  

Depending on the user, the client can be connected to a remote Docker daemon, or both can run on the same system. 

The communication between the two is done through REST API using a network interface. Docker Compose is also a client that helps with applications with several containers. 

Docker Components

Components of Docker:

The Docker components are divided into two categories; basic and advanced. The basic components include Docker client, Docker image, Docker Daemon, Docker Networking, Docker registry, and Docker container, whereas Docker Compose and Docker swarm are the advanced components of Docker. 

Basic Docker Components:

Lets dive into basic docker components:

  • Docker Client: The first component of Docker is the client, which allows the users to communicate with Docker. Being a client-server architecture, Docker can connect to the host remotely and locally. As this component is the foremost way of interacting with Docker, it is part of the basic components. Whenever a user gives a command to Docker, this component sends the desired command to the host, which further fulfils the command using the Docker API. If there are multiple hosts, then communicating with them is not an issue for the client as it can interact with multiple hosts. 
  • Docker Image: Docker images are used to build containers and hold the entire metadata that elaborates the capabilities of the container. These images are read-only binary templates in YAML. Every image comes with numerous layers, and every layer depends on the layer below it. The first layer is called the base layer, which contains the base operating system and image. The layer with dependencies will come above this base layer. These layers will have all the necessary instructions in read-only, which will be the Dockerfile. A container can be built using an image and can be shared with different teams in an organization through a private container registry. In case you want to share the same outside the organization, you can use a public registry for the same. 
  • Docker Daemon: Docker Daemon is among the most essential components of Docker as it is directly responsible for fulfilling the actions related to containers. It mainly runs as a background process that manages parts like Docker networks, storage volumes, containers, and images. Whenever a container start up command is given through docker run, the client translates that command into an HTTP API call and returns it to the daemon. Afterwards, the daemon analyses the requests and communicates with the operating system. The Docker daemon will only respond to the Docker API requests to perform the tasks. Moreover, it can also manage other Docker services by interacting with other daemons. 
  • Docker Networking: As the name suggests, Docker networking is the component that helps in establishing communication between containers. Docker comes with five main types of network drivers, which are elaborated on below.
    • None: This driver will disable the entire networking system, hindering any container from connecting with other containers. 
    • Bridge: The Bridge is the default network driver for a container which is used when multiple containers communicate with the same Docker host. 
    • Host: There are stances when the user does not require isolation between a container and a host. The host network driver is used in that case, eradicating this isolation. 
    • Overlay: Overlay network driver allows communication between different swarm services when the containers run on different hosts. 
    • macvlan: This network driver makes a container look like a physical driver by assigning a mac address and routing the traffic between the containers through this mac address. 
  • Docker Registry: Docker images require a location where they can be stored and the Docker registry is that location. Docker Hub is the default storage location of images that stores the public registry. However, registries can either be private or public. Every time a Docker pull request is made, the image is pulled from the desired Docker registry where it was the same. On the other hand, Docker push commands store the image in the dedicated registry. 
  • Docker Container: A Docker container is the instance of an image that can be created, started, moved, or deleted through a Docker API. Containers are a lightweight and independent method of running applications. They can be connected to one or more networks and create a new image depending on the current state. Being a volatile Docker component, any application or data located within the container will be scrapped the moment the container is deleted or removed. Containers mostly isolate each other and have defined resources. 

Docker Advanced Components:

  1. Docker Compose: Sometimes you want to run multiple containers but as a single service. This task can be accomplished with the help of Docker compose as it is specifically designed for this goal. It follows the principle of isolation between the containers but also lets the containers interact with each other. The Docker compose environments are also written using YAML. 
  2. Docker Swarm: If developers and IT admins decide to create or manage a cluster of swarm nodes in the Docker platform, they can use the Docker swarm service. There are two types of swarm nodes: manager and worker. The manager node is responsible for all the tasks related to cluster management, whereas the worker node receives and implements the tasks sent by the manager node. No matter the type, every Docker swarm node is a Docker daemon and communicates through Docker API. 

Docker Community Edition (CE) VS Docker Enterprise Edition (EE)

Docker comes in two variants: Docker Community Edition (CE) and Docker Enterprise Edition (EE). Launched in 2017, the Docker EE merged with the existing Docker Datacenter and was specifically created to fulfil the business deployment needs. On the other hand, the free-to-use Docker CE is more about development

This information was just the tip of the iceberg and there is a lot to unveil about the differences between the two. Here are all the significant differences between the two versions of Docker. 

  1. Purpose: The Docker Community Edition is an open-source platform for application development. This platform is aimed at developers and operations teams who want to perform all the tasks by themselves through containerization. On the other hand, the Docker Enterprise edition is made for mission-critical applications. 
  2. Functionalities and Features: Regarding core functionalities, then both Docker CE and EE come with similar functionalities. Though Docker CE will serve all the primary requirements of a developer, certain additional features come with Docker EE. These additional features include running certified images and plugins, leveraging the Docker datacenter with various levels of options, receiving vulnerability scan results, and official same-day support. 
  3. New Releases: Both Docker CE and EE have major differences in how new releases are made. The Docker CE has two different release channels; edge and stable. Edge releases are made available every month, but they may have a certain issue with them, whereas stable releases are released once every three months and are always stable. In contrast, the new releases for Docker EE are made available once every three months, and every release is supported and maintained for a year. 
  4. Pricing: The final difference between the two lies within the pricing. The Docker CE is entirely free of cost for its users. However, the pricing per node per year starts from $1500 and goes up to $3500 per year. Also, the users can get one month’s free trial of the Docker EE. 

Docker Development Best Practices:

There is no denying the fact that Docker has revolutionized software development and delivery. Software delivery can be improved if the best practices are used. Below elaborated are the best practices of Docker that will help you in getting the desired results.

  1. Keep the Image Size Small: Several official images are available when picking a Node.js image. Operating system distributions and version numbers are the primary differences between these images. When choosing the right image, it is worth noting that the smaller size image will make the entire process faster. A smaller size image will consume less storage, making the image transfer faster and easier. 
  2. Keep Docker Updated: When you begin work on a new project on Docker, you need to make sure that you update it to the latest version. The images that you use should also be the latest. This basic practice is part of the best ones because it will provide you with the latest features while minimizing the overall security vulnerabilities.
  3. Only Use Official images: If you wish to develop a Node.js application and run it as a Docker image, then you should use an official node image for the app rather than using a base operating image. This practice will provide you with a cleaner Dockerfile as the base image will be made with the right practices.

Docker Services:

Docker is a vast topic to elaborate, and this article covers all the crucial elements of this software development platform. There are innumerable ways how Docker can be used, but the goal of every team working on Docker remains the same; to boost software delivery. 

ThinkSys Inc. is a renowned name in the DevOps industry, offering all the primary DevOps services, including Docker. Whether you want to learn more about Docker or want to implement the best Docker practice in your organizations, all you have to do is connect with ThinkSys Inc. Our professionals will take all the measures to get the best results for your organization using Docker.

Below explained are the Docker services provided by ThinkSys Inc. Our team is proficient in delivering all these services effectively to make sure that your Dockerized environment remains functional. 

  • Docker Consulting:Get the best guidance on Docker from the experienced professionals from ThinkSys who will recommend the right methodologies to implement Docker in your organization,
  • Docker Implementation:Want professional assistance in implementing Docker? ThinkSys Inc. is a pioneer in helping organizations implement Docker. Our experts will run an analysis of your requirements and the goals you want to achieve and create an implementation blueprint based on that. 
  • Container Management:Manage your containers for mobile and web-based apps that use Kubernetes effectively. Our container management service includes automatic container creation, deployment, and scaling. 
  • Docker Customization:Run your Docker containers with a personal touch by using custom plugins and API which can be personalized depending on your organizational requirements or expectations. 
  • Docker Container Management:Docker containers may have certain issues that need immediate attention. Our Docker container management service will help you as our experts will analyze and detect issues in the containers. Moreover, managing the containers to keep their performance top-notch is also part of this service.
  • Docker Security:Improve the security of your current Dockerized environment with ThinkSys’ Docker security service. By using the industry-leading Docker security tools and practices, we make sure that your Docker environment is always secure. 
  • Docker Support:Have any trouble post Docker implementation? ThinkSys offers Docker support whenever you want so that your application deployment is never halted due to any unforeseen circumstance. Our qualified team will identify and fix the issue as quickly as possible.

Connect with our Docker Experts Now.

FAQ: Docker

Docker components are divided into two parts; basic and advanced. The basic Docker components are:

  • Docker Client.
  • Docker Image.
  • Docker Daemon.
  • Docker Networking.
  • Docker Registry.
  • Docker Container.

Advanced Docker Components:

  • Docker Compose.
  • Docker Swarm .

Developers create containers to deploy applications in isolation. Docker helps developers in creating containers easier, and safer. It can be used by using the right commands, making it extremely easier for a developer to build, deploy, update, and run containers.

A Docker Engine is a containerization technology that acts as a client-server app with a CLI client or a server with a Docker Daemon process. This open-source technology is specifically created for containerizing apps.

containerd is a daemon process that helps in creating, starting, destroying, or stopping containers. In other words, it is a container runtime that handles the entire lifecycle of the container on either a virtual machine or a physical machine.

Though some people may think containerd and Docker are the same, the biggest difference between them is that containerd is just container runtime whereas Docker is an assortment of different technologies that function with containers.

A Docker Hub is a repository used by Docker users to create, store, share and test container images with the team. With Docker Hub, users can create private repositories and have access to public image repositories as well. 

Docker is a highly versatile software platform that can be used with microservices. Docker can be used to create images, deploy and run microservices in a single host or one microservice in one host.

Though there are certain features that are unique to Docker, still several good alternatives exist including:

  1. Buildah.
  2. RunC.
  3. LXD.
  4. Podman.
  5. Kaniko.

Docker has gained excellent traction in recent years and legions of goliaths have also started using it. Some of the biggest names that use Docker are:

  1. The New York Times.
  2. PayPal.
  3. Spotify.
  4. eBay.
  5. The Washington Post.

Docker is surely considered the future of virtualization and has even proved itself to be better than virtual machines in many ways. With features like better security, a private platform, lesser costs, and better application deployment, it can be said that Docker has a bright future in virtualization.

There are numerous differences between an image and a container in Docker. However, the biggest one is that a container should run an image to be created but images can exist without any container. Apart from that, images do not need any computing resources whereas containers need to have computing resources to function.

Though Docker containers are capable of running multiple applications, experts always advise running a single application in a Docker container. The reasons for doing so are to attain better isolation, make it easier for building, testing, and scaling the application, and simpler to manage.

Podman and Docker are both container management platforms, but there are major differences between the two. Docker uses Daemon as a component whereas Podman follows a daemonless architecture. Apart from that, Docker supports Docker-swarm whereas Podman does not support Docker-swarm.

Related DevOps Blogs:

  1. Best DevOps Automation Tools 2022.

  2. Top 15 DevOps Metrics and KPI’s.

  3. Azure DevOps Pipeline Guide 2022.

  4. DevOps On Cloud In 2022.

  5. How Microservices Comes Brilliantly With DevOps.

  6. How Offshore Development has Changes with DevOps?

  7. Application Development With Microservices In DevOps Age.

  8. Understanding the Kubernetes Architecture.

best devOps automation tools

Best DevOps Automation Tools 2022

When it comes to attaining the utmost outcome in an organization, a collaboration between two teams is necessary. With the rapid change in dependency on IT organizations, working together for better product development becomes essential. DevOps is a practice implemented in an organization that boosts the collaboration between the development and operations team to enhance their product development and release program faster. 

Depending on the project, the organization can create different teams and set up a better collaboration to reduce the software development lifecycle. With the rising implementation of DevOps, organizations take all necessary measures to increase their effectiveness. Automation is an integral part of every IT organization. Automation reduces the possibility of errors due to human intervention, but it also helps build CI/CD pipelines.  

Using DevOps automation tools, organizations can accomplish their product development goals faster and effectively. Often, there are stances when DevOps automation tools are confused with Infrastructure-as-Code tools. Though they are pretty similar, DevOps automation is a broader spectrum, and IaC tools are just a part of it. 

DevOps automation tools have become a crucial aspect of success for product development. With that in mind, it is pivotal to pick the right tools as per the requirements. This article elaborates on the best DevOps automation tools for 2022 that will take your infrastructure, CI/CD pipelines, and CI/CD automation to the next level. 

best devOps automation tools

DevOps Automation Tools for Infrastructure Management:

  • AWS CloudFormation: Amazon Web Services provides different resources for an organization. However, managing them manually is a sheer task. AWS CloudFormation is an infrastructure management tool by Amazon that helps enterprises create and manage different AWS resources in an uncomplicated way. With AWS CloudFormation, you can create and model your applications through automation. A stack is a group of AWS resources that can be used to create or update AWS resources.Furthermore, CloudFormation also allows you to handle these resources or the entire infrastructure in a template or a text file, making this task highly feasible.The most prominent feature of this tool is the configuration of the remote state, which comes out of the box. CloudFormation StackSets allows the user to use a single template to use the same collection of AWS resources on different accounts and regions. Whether you want to use JSON or YAML or design visually, this tool provides the provision to model the files in any way you like.

    Popular languages, including .NET, Python, and Java can be used for defining cloud environments in this tool. If you want to build serverless applications faster, you can use the AWS Serverless Application Model, where you only need to write minimal code lines per resource.However, a lack of a central place for sharing templates is missing in AWS CloudFormation. Apart from that, modularization is also a bit complicated as you may need to use import or export out values between modules or nested stacks.

  • Chef: Chef is another cloud infrastructure management tool that can assist a developer in orchestrating servers in a cloud. This open-source tool makes the job of both developers and system admins easier by moving the process to a CD model through automated workflow. Based on Ruby, it uses cookbooks where the actual infrastructure coding occurs.Here the developer can write the infrastructure code depending on the domain-specific language. Each cookbook will include an assortment of different configuration data. Moreover, a cookbook also stores Chef Recipes that can be created and edited through the workstation in the tool. This workstation also stores infrastructure configuration. An agent should be configured which will run on the servers.

    This agent will run the already done configurations on the server by pulling the cookbooks from the chef master server. The Chef comes with a knife utility, making it compatible with different cloud technologies for distributing the infrastructure in a multi-cloud environment. However, the biggest hindrance while using this tool is knowledge of Ruby. As Chef is based on Ruby, the user must know the language to get the most out of it. 

  • Pulumi: Pulumi is a tool for managing, designing, and deploying resources on cloud infrastructure. This open-source tool is compatible with different types of hybrid, public, and private clouds for all the major cloud providers, including Kubernetes, OpenStack, AWS, Google Cloud, and Azure.Whether it is creating traditional infrastructure elements like databases and virtual machines or designing the latest cloud components like clusters and containers, Pulumi can be used to obtain tremendous results. You can use renowned programming languages like TypeScript, Go, .NET, and Python when managing the code. Policy compliance is a vital task that can be done automatically by Pulumi.

    This tool builds a preview and monitors whether it meets the set compliance before creating resources. This tool uses IaC. Managing hosting services and cloud infrastructure is child play. Even after several practical features, Pulumi lacks behind in structuring large projects easier. While performing this task, this tool will structure large projects as either a single large project or several small projects. Either way, deserializing stack references while mapping multiple resources becomes highly complicated.  

  • Ansible: Developed by Ansible Inc, Ansible is a DevOps automation tool for actions like intraservice orchestration, configuration management, provisioning, and application deployment. Being an open-source tool, Ansible is easy to use and can be used without learning special coding skills. Even though it is simple to use, it does not lack any features and can complete complex IT workflows.Ansible is written in PowerShell, Python, and Ruby, making it compatible with leading operating systems, including Windows, macOS, and Linux. There is no code running on the controlled nodes; it is often called one of the tools with agentless architecture. Ansible saves a lot of resources on the server as there is no need for any third-party software for its installation.

    In addition, when Ansible is not managing any nodes, it does not consume any resources on the node machine, saving additional resources. With high customization possibility and flexibility, Ansible can be used to orchestrate an application environment wherever deployed.  

  • Terraform: Created by HashiCorp in 2014, Terraform is an IaC tool that allows the users to define on-premises resources and cloud in readable configuration files which can be used, shared, and versioned. Compatibility is never an issue in supporting all major operating systems like OpenBSD, macOS, FreeBSD, Windows, Solaris, and Linux.With this tool, you can create and manage resources on different services and cloud platforms, including Google Cloud, AWS, Azure, Oracle Cloud, IBM Cloud, etc. Terraform uses application programming interfaces to perform, allowing the users to work with any API or service they want. Using a consistent workflow, you can manage the entire infrastructure through Terraform.

    The workflow includes writing, planning, and applying stages where defining the infrastructure, reviewing the changes, and updating the state files take place respectively.Terraform is an excellent tool that lets the user manage and track any infrastructure, standardize configurations, automate the desired changes, and enhance collaboration. If multiple people work on a single infrastructure, Terraform lets the user lock modules where only a single user can change the infrastructure.

    It also provides a resource graph that gives an in-depth overview of the infrastructure management tasks.Moreover, with module count, you will always know the number of modules in an infrastructure. The significant shortfall with this tool is that it uses HCL as the primary language, which every user has to learn before beginning this tool. Moreover, if anything goes sideways, there is no automatic rollback and error handling feature in Terraform.

     

DevOps Infrastructure Automation Toolset

A set of different software or software utilities is a toolset. While working with an arduous practice like DevOps, there is not a single tool that can fulfil every need. Considering that fact, many organizations prefer using toolsets which are tailor-made sets of different tools with unique features that will cater to their needs. Here are all the different DevOps infrastructure automation toolsets that you should consider. 

  • Docker: Docker is a DevOps tool for infrastructure automation, used to package the created application into small containers, ensuring that the application works without any issues in different environments. In short, this tool is specially designed for containerization that makes the creation, deployment, and running of applications easier through containers. Released in 2013, Docker has become one of the most renowned platforms for containerization.Not just developers but system administrators are also benefited from Docker. The code can be written without worrying about testing, and infrastructure can be scaled up and down through Docker whenever the system admin wants to.

    The automatic rollback feature available in Docker is an excellent way to deploy applications rapidly. With these significant benefits and a steep rise in usage of Docker, containerization has been replaced by virtual machines by many organizations. 

  • Python: Automation scripts are used to leverage scripts in a framework that optimizes specific tasks. These scripts include a launch point, binding values, and a source code. As writing a code requires knowledge of a particular language and no programming language is the same, the most preferred one for automation scripts in Python.Being an open-source language, it is the preference of several veteran developers. The developer can use APIs to connect and manage infrastructure resources through this programming language. 
  • Kubernetes: As containers are lightweight and large in number, running them can be a major skirmish. Container orchestration automates the tasks required to run containerized services, including deployment, load balancing, and networking.Kubernetes is one of the most widely used container orchestration platforms. With this open-source platform, developers can quickly build containerized services and applications. Kubernetes comes with several features for container orchestration, including automated rollback and rollout, load balancing, automatic bin packing, storage orchestration, and self-healing. Moreover, it supports multi-cloud orchestration as well. 
  • Terraform: Even though Amazon Web Services is one of the leading environments presently, there are still legions of organizations using other cloud environments like IBM Cloud, Google Cloud Platform, or Microsoft Azure. If your organization uses a cloud environment other than AWS, Terraform is undoubtedly one of the best DevOps automation tools for automating infrastructure management. 
  • CloudFormation: CloudFormation is considered among the best DevOps tools for infrastructure automation in the AWS environment. With smooth integration with AWS, you can automate the resources and infrastructure of your organization with CloudFormation.Some also consider it an IaC tool as setting up automation and deployment of different IaaS services is possible with this tool. In other words, no matter what service you are running on AWS, it can be automated through this tool.

Continuous Integration/Continuous Pipeline Automation Tools

Earlier, software development was based mainly on the waterfall model. However, things have changed since the arrival of DevOps in the mainstream software development industry. One of the primary practices in DevOps is continuous delivery which is achieved through a continuous integration/continuous deployment approach. The CI/CD approach integrates automation in every software development and delivery stage, reducing the overall software development lifecycle. 

In the CI/CD approach, different teams work collaboratively to better the software development process. CI allows the developers to deploy software quickly and automatically issues all the major bugs. Moreover, every CI server can run legions of builds rapidly and automatically. On the other hand, CD allows the ops teams to deploy software automatically. The CI/CD approach enables enterprises to minimize the development cycle and deliver the program quickly. 
Almost every DevOps environment uses CI/CD tools to achieve the best outcome. With that in mind, here are the most widely used DevOps automation tools for CI/CD pipelines.

  • Jenkins: Earlier, software development was based mainly on the waterfall model. However, things have changed since the arrival of DevOps in the mainstream software development industry. One of the primary practices in DevOps is continuous delivery which is achieved through a continuous integration/continuous deployment approach.The CI/CD approach integrates automation in every software development and delivery stage, reducing the overall software development lifecycle.In the CI/CD approach, different teams work collaboratively to better the software development process. CI allows the developers to deploy software quickly and automatically issues all the major bugs. Moreover, every CI server can run legions of builds rapidly and automatically. On the other hand, CD allows the ops teams to deploy software automatically.

    The CI/CD approach enables enterprises to quickly minimize the development cycle and deliver the program. Almost every DevOps environment uses CI/CD tools to achieve the best outcome. With that in mind, here are the most widely used DevOps automation tools for CI/CD pipelines.Apart from that, Packer is compatible with different plugins that can expand its overall functionalities.

    Packer can have components like data sources, builders, and provisioners by integrating various plugins. Builders can help create machines and build correspondent images whereas provisioners can help in the installation and configuration of software and generated images.

  • Packer: Packer is an open-source tool effective for packaging the dependencies and building deployable virtual machine images. With this tool, you can create similar machine images using a single configuration source for several platforms. Moreover, you can generate new machine images for different platforms and test and verify the infrastructure changes for continuous delivery. Having identical images will let you run development, production, and staging environments on different platforms. 
  • AWS CodePipeline: Releasing software is a crucial step in DevOps practice. The AWS CodePipeline is a CD service that can help you model, visualise, and automate the steps necessary for a software release. You can use this service to automate all actions required to release your software changes continuously. When you change the code, you can define a consistent set of steps through this service.IT will automatically run the stage as per the given criteria, ensuring a consistent release process.Apart from that, AWS CopePipeline is compatible with third-party version control systems to make software delivery faster and better. The entire service is uncomplicated to set up and does not require you to manage any physical servers. You can also define the structure of a pipeline through a JSON document.

    The architecture of this service has three elements; AWS CodeBuild, CodeCommit, and CodeDeploy.Even though AWS CodePipeline is a feature-rich service, it is entirely based on the pay-to-use model. The user will have to shed money for the duration for which they use the service. Due to this reason, the overall service may seem pricey as compared to its peers. While it may not be an issue for some, the plugin support is still limited in AWS CodePipeline. 

  • CircleCI: Developed in 2011, the CircleCI is one of the largest shared CI/CD cloud-based tools, which provides excellent control and flexibility for managing CI/CD pipelines.  Being supported by services of major players, including Slack, AWS, and Atlassian, compatibility will never be an issue with this CI/CD automation tool for DevOps.  Furthermore, it supports programming languages like Python, JavaScript, Ruby, C++ along with platforms like Windows, Linux, and macOS. With the FedRAMP certification and compliance with SOC 2 Type II, this tool is sure to provide the best security.  Moreover, restricted contexts, audit logs, and similar features offer you extensive control over your code.  Whenever you modify the existing code in CircleCI, the tool automatically triggers the CircleCI pipeline. This trigger will begin testing on the desired container or virtual machine automatically, and if any issue is found, the concerned team will be notified immediately and without any manual intervention. 

    As every task is created in a single circle.YAML file, you can take backup immediately and without much effort.  Even though the initial configuration of this tool is simple, it becomes complicated as the file size increases.  The lack of customization options is also a crucial issue with CircleCI.  

  • GitLab: Created by GitLab Inc, GitLab is a comprehensive DevOps platform that helps developers in every stage of the software development lifecycle. This tool embraces collaboration between different teams in an organization in every phase of the project, ensuring smooth software development and delivery.The web-based Git repository helps enterprises create and manage private and open repositories. You can even set permissions to different users as per their roles and manage these permissions automatically.

    With support for third-party plugins and APIs, you can always assure that you will never feel a lack of features with this tool. However, there are several existing bugs acknowledged by the GitLab team, which could hamper the user’s overall experience. Apart from that, the tool’s user interface may seem rocket science while reviewing. 

DevOps Automation Tools for Infrastructure Development

  • Vagrant: Vagrant is an open-source tool used for working with a virtual environment or virtual machines. Developed and managed by HashiCorp, this tool is easy for developers, designers, and operators. Vagrant allows users to use any browser, editor, or integrated development environment for reviewing or fixing bugs in the code. Moreover, integration with other configuration management tools like Chef, Puppet, or Docker is not an issue with this infrastructure management tool. It runs on virtual machine solutions, including VMWare or Virtual box and uses Vagrantfile as its default configuration file. 
  • Minikube: Minikube is a tool that will allow you to run Kubernetes locally. If you want to try Kubernetes for your regular development tasks, Minkube is the right infrastructure management tool for you. Through this tool, you can run Kubernetes on your personal computer with operating systems like Linux, Windows, and macOS. It is reliable, fast, and lightweight, allowing you to work on Kubernetes efficiently.

DevOps Automation Tools for Infrastructure Monitoring

  • Prometheus: When it comes to infrastructure monitoring tools, then Prometheus is undoubtedly among the best. With features like excellent metrics visualisation, powerful queries, accurate alerting, third-party integrations, and dimensional data, among others, it is proven to be highly efficient in monitoring infrastructure. The open-source tool supports Kubernetes monitoring along with a Linux server. The Prometheus comes with an in-built alert manager which handles the monitoring metrics’ alerting setup.  
  • Datadog: Datadog is a DevOps team analytics and monitoring tool that can help identify the teams’ overall performance during software development. This tool is proficient in monitoring databases, servers, and tools for deployment. With support for leading cloud providers like Google Cloud Platform, AWS, and Microsoft Azure and compatibility with Linux, Windows, and macOS, you can use this monitoring tool with all the major providers and platforms. To integrate this tool with services, programming languages, and tools, you need a REST API. 

Which is the Best Automation Toolset for Large Enterprises?

As stated before, DevOps is a herculean practice that includes adopting different techniques. Though many significant organizations now use DevOps in some way or the other, their business requirements from DevOps are different. Due to this reason, there cannot be a single best automation toolset for DevOps. Having the right set of automation tools in DevOps is highly critical as it directly impacts the outcome of DevOps. When it comes to picking the right toolset, there are specific considerations, including a budget, existing infrastructure, business goals, and the organization’s culture. 

For instance, large enterprises can use Jenkins with GitLab to meet their CI/CD requirements. They can manage their CI/CD pipeline from a single platform. On the other hand, small or medium-sized organizations can go for CircleCI due to its budget-friendly approach while providing excellent features. However, suppose you are inclined towards a particular service provider like AWS. In that case, you can go for AWS CodePipeline as it will be compatible with AWS while giving you the option to integrate the tool with other services. 

How Can ThinkSys Help In Selecting the Right Tool?

Without a doubt, picking the right toolset is a significant task, and messing up in this step could be catastrophic for the entire DevOps team and the software. If you are unsure which toolset will be suitable for you, then ThinkSys Inc DevOps team can guide you in making the right choice. Our engineers will understand your requirements and specifications to analyze your goals and what you are looking to achieve. Our experts will curate an automation toolset for DevOps by undergoing the study. Not only that, but we will also assist you in the appropriate implementation and management of the toolset. 

BooK Your DevOps Consultation With Our Experts Today 

Frequently Asked Questions(FAQ)

Picking the right automation tools is a critical task completed by understanding specific considerations. Before selecting an automation tool, you need to know your organization’s goals, requirements, uses of the tool, and budget. After understanding all these factors, you can pick the right tool for yourself. On the other hand, you can connect with ThinkSys Inc for expert consultation on DevOps Toolchain. 

DevOps automation tools can be used in every phase of the software development lifecycle. Whether it is code generation, infrastructure automation, deployment automation, CI/CD pipelines, or infrastructure monitoring, DevOps tools are proven to help embrace automation in DevOps. 

Related Blogs:

  1. Top DevOps Metrics and KPI’s.

  2. Is DevOps Initiative Pushing your Cloud Bills?

  3. Azure DevOps Pipeline.

  4. How Microservices Comes Brilliantly With DevOps?

  5. How Offshore Development has Changes with DevOps?

  6. Agile, DevOps Or Others? Which Methodology is right for You?

  7. Test Automation In DevOps World.

Kubernetes Architecture

Understanding the Kubernetes Architecture

Developed by the tech giant Google, Kubernetes is an open-source platform used as a container orchestration system that aids in automating, managing, and scaling software development. Kubernetes is growing swiftly in the IT infrastructure within the organizations, but why is it happening? To understand that, it is essential to know the traditional method of running applications. In the traditional methods, its impossible to define resource boundaries for any application running on a physical server. This reason led to issues with resource allocation. The situation worsened when the organization needed to run more than one application on a physical server. 

In that case, a running application would consume most of the resources whereas the remaining apps may not receive optimum resources, resulting in poor performance. The only solution left was to run a single application on a single physical server, but that was highly expensive and inefficient.

Later came virtualization, where a virtual machine is created which runs multiple applications on a single physical server. Since its inception, VMs have drastically reduced the usage of traditional methods and saved a lot of resources and efforts of the users. 

Kubernetes is similar to virtualization, where containers are used. These containers are lightweight and come with all the major components of a virtual machine but are portable on clouds. Working on a container requires an entire architecture deployment. This article will explain all about Kubernetes architecture clearly. 

What is a Container Orchestration System?

A container orchestration system helps in orchestrating major container management tasks. Powered by a containerization tool that handles the lifecycle of a container, the container orchestration system can help in tasks like deployment of a container, creation, and even termination of a container. A container orchestration system is beneficial for the organization as it helps manage the complexities that containers bring with them. Apart from that, this system enhances the overall security of containerized applications by automating several tasks, reducing the probability of human errors.

The container orchestration system is highly beneficial when there are legions of containers distributed across several systems. Such a situation makes managing these containers highly complicated from the Docker command line. With a container orchestration tool, all the container clusters in the environment can be handled as a single unit, making it feasible to manage. All the tasks including starting, running, and termination of numerous containers can be done through a container orchestration system

What is Kubernetes Architecture?

A Kubernetes architecture is a cluster used for container orchestration. Each cluster contains a minimum of one control plane and nodes. In a cluster, a control plane is responsible for managing the cluster, shutdown, and scheduling of compute nodes depending on their configuration and exposing the API.  A node can be a physical or virtual machine with a Linux environment that runs pods.

Kubernetes Architecture

    • Control Plane:-

The control plane can be considered as the brain of the entire Kubernetes architecture cluster as it is the one that directly controls it. Additionally, it keeps a data record of the configurations added along with the Kubernetes object states. The control plane has three primary components: kube-scheduler, kube-apiserver, and kube-controller-manager. These all collaboratively ensure that the control plane is performing as it should. Moreover, they can either have a single master node or can be replicated to several master nodes. The replication of these components is done to attain their high availability in case of any fault. 

Components of Control Plane:

The Control plane is an essential part of Kubernetes architecture. As stated before, it comprises of several different components, and all of them are explained below. 

      1. Scheduler: Also known as kube-scheduler, it keeps an eye on any new requests received from the API server. Moreover, it analyses the node qualities, ranks them, and deploys the pods depending on the suitability of the node. Any request received from the API server will be allocated to the healthiest node. However, if there are no healthy or suitable nodes, the pods are put on hold until any suitable node is available.
      2. API Server: The API server is the communication center of the control plane and the only part of the plane where the user can interact directly, ensuring that the data is stored in the cluster as per the service detail agreement. User interfaces and external communications pass to the API server. It also receives the REST requests to pods, controllers, and services regarding any modification.
      3. Controller Manager: As the name suggests, the controller manager performs different controller processes in the background or manages the controllers. Running a different controller is done to perform regular tasks and to regulate the cluster’s shared state. If a service configuration modification is done, the controller manager will identify the change quickly and begin taking the right action for the new state. Node controller, job controller, service account controller, and endpoints controller are among the most widely used types of controller managers.However, another controller manager handles the existing cloud technologies in the cluster. The cloud controller manager can only function on the controllers specific to a cloud provider and allows the user to link the API of the cloud provider with the cluster.

        There are three types of controller managers with cloud provider dependencies. The first is the node controller, which is used to check the cloud provider and define whether the node is deleted in the cloud after being unresponsive or not. The second one is the service controller used to create, delete or update a load balancer. Service controller type can be used to set up routes in the existing cloud infrastructure. Third one is Route controllers: directly affect the communication between containers of different nodes in a Kubernetes architecture. In simpler terms, the route controller manages the traffic route in the existing Kubernetes infrastructure. However, the route controller is only applicable in Google Compute Engine clusters.
    • Key-Value Store:-

Also known as etcd,the Key-Value Store is used by Kubernetes as its database to keep a backup of the entire cluster data including configurations and states. As the etcd is accessed through the API server, it becomes consistent and accessible for the users. With the ease of access, the key-value store can be configured externally or even a part of the control plane.

Essential Components of Kubernetes Cluster Architecture:

The control plane manages the cluster nodes responsible for running the containers. Every node runs a container runtime engine and acts as an agent to communicate with the primary Kubernetes controller. In addition, other components for service discovery, monitoring, and logging are also done by these nodes. Being directly related to the control plane, knowing about the components of Kubernetes architecture is crucial. 

    • Nodes:

Nodes can be defined as either physical servers or virtual machines where Pods are places for execution in the future. Every cluster architecture comes with a minimum of one compute node, but there can be multiple nodes, and it varies with the capacity needs of the architecture. If a cluster capacity is scaled, it is necessary to orchestrate and schedule pods to run on nodes. Making it simpler, nodes are the primary workers that connect several resources including storage, networking, and computing in the architecture. Nodes are classified into two different types: Master and Worker Nodes.

      • Master Nodes: A master node is entirely made up of control plane binaries responsible for control plane components. In most cases, a cluster will have over three master nodes so that it can reach the goal of high availability. 
      • Worker Node: A worker node will have components like kube-proxy, kubelet, and container runtime which lets it run the desired containers. With that in mind, the control plane is entirely responsible for managing this type of node. 

Components of Kubernetes Nodes:

      1. Kube-proxy: Kube-proxy or the network proxy is responsible for maintaining communication between pods and network sessions inside or outside the cluster on each node. It also uses the operating system packet filtering if it is available in the nodes. Managing IP translation, network rules, load-balancing on all pods, and routing are among the functions of this node component. Moreover, it makes sure that every pod attains a distinctive IP address and containers in the same pod share the same IP.  
      2. Kubelet: Every container described in PodSpecs should run adequately for the best outcome. Kubelet is an agent present in every node and its primary task is to make sure that these containers are continuously working as they should. 
      3. Container Runtime: Every worker node comes with a container runtime engine used to run the container. This software accomplishes this task and starts or stops the container depending on the deciding factors. Docker, Container Runtime Interface, and containerd are some of the industry-leading container runtime software. 
    • Pods:

Pods are responsible for encapsulating the application containers, network ID, storage resources, and all the remaining configurations for running the containers. Though they are controlled as a separate application, pods are one or multiple containers that share data and resources. 

    • Volume:

Another significant component of Kubernetes architecture is the volume applied to the entire pod. Volume is linked to all the containers in the pod and ensures that the data is saved. Moreover, a single pod can have several volumes depending on the pod type. A volume ensures that the data is preserved and can only be eradicated upon elimination of the pod. 

    • Deployment:

The deployment controller updates the environment and describes the pod’s desired state in the YAML file. It is responsible for updating the current state with the desired state in the deployment file. In short, it is a deployment method for containerized application pods.

    • Service:

Sometimes replicating a controller can kill the existing pod and commence a new set. Moreover, Kubernetes does not claim that a physical pod will remain alive in any such stance. Service depicts a set of pods that lets pods send a necessary request to the service. The great thing is that this does not require keeping track of any physical pod. 

    • Namespace:

Environments with multiple teams, projects, and users may need isolation which they can attain from Namespace. A resource quota is allocated to a namespace so that it does not use more than its share of the physical cluster. Moreover, the resources within a namespace should be distinctive, and no namespace can access resources from any other namespace.  

    • ConfigMaps and Secrets:

ConfigMaps is used for storing commonly used or non-confidential data in key-value pairs. With this component, you can make your app’s portability easier by decoupling configurations specific to an environment from container images. The data can be entire configuration files or small properties. In a Kubernetes architecture, both ConfigMaps and Secrets let the user change configuration without the need for an application build. Though both of these terms are similar, there are several differences. The foremost one is data encoding, where Secrets uses base64 encoding to store data. Furthermore, Secrets are mostly used for storing passwords, certificates, pull secrets, and other similar data types.  

    • StatefulSets:

Deployment of a stateful application in a Kubernetes cluster is tricky due to its replica architecture and fixed Pod name requirement. StatefulSets is a workload API object that can run stateful apps as containers in a Kubernetes cluster. It also handles the deployment of Pods based on an identical container specification. In other words, controllers implement uniqueness properties and run stateful applications in a Kubernetes architecture.  

    • Replication Controllers:

A ReplicaSet let you know about the number of times a pod is required in architecture. A replication controller handles the entire system to match the number of pods in a ReplicaSet with the number of working pods in the architecture.  

    • Labels and Selectors:

Labels are value pairs linked to objects like pods used to showcase the characteristics or information relevant to the users. These can either be added while objects are created or modified later. Moreover, they can also be used for organizing or selecting subsets of objects. However, many different labels may have the same name, confusing the user while identifying a set of objects. With that in mind, Selectors are used to help group the objects. Set-based and equality-based are the two types of selectors where filtering is done based on a set of values and label keys, respectively.  

    • Add-Ons:

Like any other application, add-ons are used in a Kubernetes architecture to extend its functionality. Add-ons are implemented through services and pods, and they implement Kubernetes cluster features. Different types of add-ons can be managed by ReplicationControllers, Deployments, and many others. Some of the popular Kubernetes add-ons are Web UI, cluster-level logging, and DNS.  

    • Storage:

A Kubernetes storage is mainly based on Volumes divided into two: persistent and non-persistent. Persistent storage supports different storage models, including cloud services, object storage, and block storage. A Kubernetes storage comes with non-persistent storage by default. Such storages are part of a container in a Pod which is stored in a temporary storage space of the host and will exist along with the pod.   

What is Docker?

Docker is an open-source platform for containerization that allows the user to separate the software or app from the current infrastructure, reducing the software delivery time. Often there is a delay between writing the code initially and production running. Using Docker methodologies for tasks like shipping, deploying, and testing the code, can minimize this delay extensively.

Through Docker, you can create a container and run the application on it. The created container will be a secluded environment and you can run several containers concurrently. Using Dockers, the developers can write the code locally and even share the same. Apart from that, Dockers can be used to push applications into a testing environment for both manual and automated tests. If there are any bugs found while testing, developers can resolve the issues in the development environment and repeat the process for testing.

Docker architecture has three components; Docker Software, Docker Objects, and Docker Registries. Dockers are compatible with all the leading OS, including Linux, Windows, and macOS, when it comes to operating system support.

What is a Container?

Containers are software packages that have every element essential for software to run in an environment. Whether it is a public cloud, a personal computer, or a private data center, containers can run in any environment as they can virtualize an entire operating system. Containers allow the developers to run several apps in a single VM and move them across the different environments with ease. Even after having all the software dependencies, containers are extremely lightweight, so they are heavily used in software development.

The closest competitor of a container is a virtual machine. However, compared to VMs, containers share a single operating system kernel, leading to lesser resource consumption. Furthermore, they do not need an entire OS to perform, vastly reducing a container’s size.

Features of Kubernetes:

Kubernetes is not just an orchestration tool but offers many valuable features. Having five different functionalities of Kubernetes architectures ensures that it becomes an overall package offering several features. Here are all the primary functionalities that you can get with Kubernetes. 

Features#1: Rollbacks –

There are stances when the desired changes remain incomplete, which can dramatically impact the end-user’s experience. Kubernetes comes with an automated rollback feature that can reverse the changes made. Furthermore, it can also switch the existing pods with new pods and change their configurations.

Features#2: Self-Healing-

Issues can occur at any moment and allowing connections to an unhealthy pod could be catastrophic. Kubernetes constantly keep an eye on the pod’s health and ensure that they are working perfectly. In case any container fails to function, it can automatically restart. However, if that does not work, the system will hinder the connection to those pods till the issues are fixed. 

Features#3: Load Balancing-

Load balancing is one of the biggest aspects of efficient utilization of resources and keeping the pods stable. By automatically balancing the load among multiple pods, Kubernetes ensures that no pod is overburdened. 

Features#4: Bin Packing-

Not just load balancing, but other practices are necessary to keep resource utilization in check. Depending on the CPU configuration and RAM requirements, Kubernetes assigns the containers accordingly so that no resources are wasted during the task.  

Features#5: Better Security-

Security is a significant concern before adopting any new technology. If the tech is proven to be secure or brings practices that ensure security, the user’s confidence increases drastically. With practices like transport layer security, cluster access to authenticated users, and the ability to define network policies, Kubernetes expands the overall security. Furthermore, it also addresses security and application, cluster, and network levels. However, certain practices like updating Kubernetes to the latest version, securing the Kubelet, reducing operational risk through Kubernetes native security controls, and securing the configuration of Kubernetes API are some of the practices that will help in extending the security even further. 

Use Cases of Kubernetes Architecture:

Use Cases#1: Cloud Migration-

The Lift and Shift method of migration is a renowned way of migrating the application along with all the data to the cloud without any changes or minimal changes. Several organizations use this method for migrating their application to large Kubernetes pods. After they become comfortable with the cloud, they break the large pod into small components to minimize the migration risk while making the most out of the cloud.

Use Cases#2: Serverless Architecture –

Serverless architecture is widely used by organizations to build and deploy a program without obtaining or maintaining physical servers. Here, a third-party server provider will lend a space in their servers to an organization. Even though it is an excellent way for many, the lock-in by such providers may be a deal-breaker for some. On the other hand, Kubernetes architecture lets the organization build a serverless platform with the existing infrastructure.

Use Cases#3: Continuous Delivery –

DevOps is all about continuous integration/ continuous delivery. Kubernetes architecture can automate the deployment when a developer builds the code using the continuous integration server, making Kubernetes a significant part of the DevOps pipeline.

Use Cases#4: Multi-Cloud Deployment –

Cloud deployments come in different types, including private, public, hybrid, and on-premise. When the data from different applications move to different cloud environments, it is complicated for the organizations to manage the resource distribution. With the automated distribution of resources in a multi-cloud environment, Kubernetes architecture makes it feasible for organizations to manage resources efficiently. 

Conclusion:

Without a doubt, Kubernetes architecture is a scalable and robust orchestration tool. This was all about the Kubernetes architecture, its components, and the features that it brings. Since its inception by Google, it has reduced resource wastage and burden on the physical servers by virtualization and orchestration. Being designed specifically for security, scaling, and high availability, this tool has fulfilled the goals and continues to do so. 

Suppose you want to migrate to cloud technologies or enhance your current cloud infrastructure using Kubernetes. In that case, all you have to do is connect with ThinkSys Inc. Migration to the cloud is not just complicated but can be expensive if done incorrectly. With the assistance from ThinkSys Inc, you will get the best migration and save your budget. Our professionals will evaluate the stability of your existing applications before migrating to Kubernetes architecture.

Whether you need Kubernetes consulting, implementation, or support, you can connect with ThinkSys Inc to get Kubernetes assistance.

Frequently Asked Questions:

Kubernetes architecture solves legions of cloud-related problems faced by an organization. This architecture can provide solutions including automated rollouts, rollbacks, autoscaling, storage orchestration, configuration management, load balancing, self-healing, and role-based access control.

Without a doubt, Kubernetes offers excellent features like great scalability, self-healing, and support for zero runtime. However, with great features, comes extensive learning. Kubernetes may surely seem complicated as the learning moves forward and some may not even learn it. The good thing is that there are a few ways to manage the operations. These options are Kubernetes-powered PaaS and Kubernetes fully managed services. The former provides cloud platforms integrated with Kubernetes. On the other hand, the latter uses fully managed services like Azure Managed Service, and Amazon Elastic Kubernetes Service.

Kubernetes is not the only one offering container orchestration. However, it is one of the best to get the job done. If you do not wish to use or are unable to use it, the closest alternatives to this architecture are Nomad and Docker Swarm. 

In that case, all you have to do is connect with ThinkSys Inc. Migration to the cloud is not just complicated but can be expensive if done incorrectly. With the assistance from ThinkSys Inc, you will get the best migration and save your budget.

Kubernetes helps in scaling and maintaining applications, and managing containerized applications on different servers. Microservice architecture is a method of building software as sets of individually deployable services. Features like containerization, usage of pods, effective cloud migration, reduction in resource costs, and workload scalability are changing the microservices architecture.

When it comes to running Kubernetes architecture on-premises, one needs to meet the following requirements:

  • A minimum of one server, but the recommended number is at least three for optimum performance of control plane components and worker nodes.
  • Having a separate server for the master components.
  • SSD.
  • Dedicated load balancer node.
  • Building services like scalable networking, persistent storage, etcd, ingress, and high-availability master nodes.

Related Blogs:

  1. Software Development KPI’s and Metrics.
  2. Software Testing Metrics and KPI’s.
  3. Azure DevOps Pipeline Guide 2022.
  4. DevOps On Cloud.
  5. Multi-tenant Architecture For Cloud Apps.
  6. Understanding the Kubernetes Architecture.
Devops Metrics and KPIS

15 DevOps Metrics and KPI’s: Measuring DevOps Success

With the rising wave of using DevOps in an organization, everyone wants to try it out and implement it to make the software deployments faster and more efficient. Without a doubt, the proper implementation of DevOps provides guaranteed results. However, taking the right decision at the right time is equally crucial in achieving the stipulated outcome. 

Even implementing the strategy that has worked previously may not provide the outcome the experts were looking for. With that in mind, monitoring the DevOps performance is highly pivotal to ensure that the results are never compromised and you always help boost the software development lifecycle. This article will elaborate on some essential metrics and key performance indicators of successful DevOps that will allow you to determine whether your DevOps culture is providing optimum results or not.  

Devops Metrics and KPIS

Key DevOps Metrics:

#Metrics 1: DORA Metrics:

The DevOps Research and Assessment, aka DORA, with their six years of research, came up with four key metrics that will indicate the performance of DevOps. These metrics are also known as The Four Keys. They rank the DevOps team’s performance from low to elite, where low signifies poor performance and elite signifies exceptional performance towards reaching their DevOps goals. Deployment Frequency, Lead time for Changes, Change Failure Rate and Mean time to Restore Service are the four pillars of DORA metrics, and these are explained in detail below. 

  1. Deployment Frequency: The deployment frequency metric gives an insight into how frequently an organization releases software to production successfully. With the implementation of CI/CD in teams, deploying the software has become more frequent than ever. Teams release software even several times a day, leading to improvements in the existing software, pushing bug fixes, and adding new features. Moreover, the frequent deployment also expands the scope of quickly attaining real-time feedback, allowing the developers to work on the next release quickly. However, the reason why deployment frequency is measured is to measure the short-term as well as long-term efficiency of the DevOps team. Tracking this metric will allow the teams to identify any underlying issues that may be causing delays in release or service. To fall under the elite category, the median number of days per week should be a minimum of three where deployment has been made. Akin to that, high, medium, and low rank’s most deployments lie between once per week, once per month, and once every six months respectively.
  2. Lead Time for Changes: Evaluating the time consumed for a committed code to move into production is the lead time for changes. Calculating lead time for changes allows the DevOps teams to understand the time taken by the team to push the committed code into production, allowing them to determine their average response time for tackling issues. Furthermore, it also depicts their effectiveness in handling the issues.The general rule of thumb is that the shorter lead time for changes is better. However, this does not apply to every project. Complex projects may consume more than the average time. The team’s more time on a complex project does not necessarily mean that the team is ineffective. It simply showcases that the complex nature of the project made the team spend more than usual time. The difference between the commitment and deployment is the lead time for changes. If it is less than one day, it is ranked as elite. However, having lead time for changes is between one day and one week, one week and one month, and one month and six months, it is ranked as high, medium, and low, respectively.
  3. Change Failure Rate: The change failure rate is the ratio of failure and successful deployments in production. With this Azure DevOps metric, the team can analyze the efficiency of their DevOps process. There are two values required to calculate this metric; the number of deployments attempted and the number of failures in production. The number of deployments can be extracted from the deployment table, ultimately providing the incidents. These incidents can be in the form of spreadsheet pipeline, bugs, labels on GitHub incidents, or any other. By using these two numbers, analyze the change failure rate percentage. Elite teams score 0-15% in this metric, whereas high, medium, and low teams’ change failure rate lies within 0-15%b, d, 0-15%c, d, and 40-60%, respectively.If the team ranks low in this DevOps performance metric, they need to make several changes in their deployment process to minimize the probability of failures and enhance efficiency. Furthermore, they need automation in the DevOps process, leading to reliable production and deployment.
  4. Mean time to Restore Service: Calculating the time taken by the organization to recover from failure in production is the meantime of restoring service. One of the most crucial DevOps quality metrics, calculating MTTR, should be a standard practice in every DevOps environment. This metric allows the team to determine the stability of the recovery process. To calculate MTTR, the DevOps team needs to know when the incident happened and when it was resolved.Elite ranked teams have a mean time to restore service of less than an hour. However, high, medium, and low ranked teams take less than a day, less than a week, and between a week and a month. In most cases, if the team is capable of resolving the issues within a day is considered optimum. Any team consuming more than that time needs to make specific changes in the recovery process, like deploying automated monitoring solutions and deploying software in small increments.

#Metrics 2: Test Case Coverage:-

Test case coverage is the preference of several veteran DevOps engineers. It assists in eradicating defects in the early stages, eliminates unwanted cases, provides better control, and ensures smoother testing cycles. Test case coverage is the method through which the team can understand whether their test cases cover the application code or not. Moreover, test case coverage will also allow them to determine how much code is exercised upon running those test cases.

For instance, if there are 25 requirements and the tests created are 250, 225 tests are executed. In that case, the test case coverage will be 90%. The team can build other test cases for underlying tests through this number. 

Test case coverage is measured based on the lines of code. If there are 1500 lines of code, among which the executed lines are 600, then the test case coverage would be 80%. The test case coverage DevOps success metric is divided into code-level, feature testing, and application-level metrics.

#Metrics 3: Code Level Metrics:

The code level metric is based on the test coverage percentage method, which showcases the percentage of executed tests out of the total tests. Several experts prefer this metric as it provides an overview of testing progress. However, there is a limitation that counting code lines do not necessarily mean that it will perform as desired. 

  • Feature Testing: Feature testing is further divided into requirements coverage and test cases by the requirement. The requirements coverage helps understand the efficacy of the test cases in covering software requirements. To calculate this metric, you must divide the number of requirements covered by the total number of requirements and multiply it by 100.
    The other one is test cases by the requirement, which is used to determine the tested features and the tests aligned with the requirement. In most cases, a requirement will have more than one test case, so it is crucial to know about any failed test cases. Afterward, the test cases for a failed requirement should be rewritten as per the requirements.
  • Application Level Metric: The application-level metric is also divided into defect density and requirements without test coverage. Defect density helps the team identify the areas where automation is required. It is measured by dividing the number of known defects by the size of the software entity. The other part of the application-level metric is requirements without test coverage. Once the required coverage is calculated, some experts may witness a few uncovered requirements. This metric allows the team to identify and eradicate the requirements not covered from all the requirements before sending them for production. This metric is essential as the team needs to know the covered requirements and which are left behind.

#Metrics 4:Mean Time to Failure: 

Mean time to failure is the average time gap between two failures. This metric is often used to determine the frequency of software failures by the DevOps team. Every team’s goal is to keep MTTF as low as possible. However, there are stances when this DevOps maturity metric may show high results. Such results indicate specific underlying issues with the development team or software quality. It may also indicate a lack of testing by the testers on the software before releasing the software update.

#Metrics 5: Mean Time to Detect:

Before fixing the issue, the team should be able to detect the same as quickly as possible. Mean time to detect is the average time consumed by the time to diagnose an issue with the software. An inexperienced or poorly skilled team may take longer than usual to diagnose an issue, whereas the MTTD should ideally be less inexperienced. Teams with poor MTTD lack monitoring on software and a significant amount of data that will help them detect the underlying issue.

#Metrics 6: Mean Time Between Failures:

The mean time between failures is the average time between two failures of a single component. Often engineers are confused between MTTF and MTBF. Even though they are quite similar as they both are about the average time between failures, MTTF is about the failure in deployment by the team, whereas MTBF is about failures in a single component. Many DevOps engineers use this DevOps quality metric to determine the stability of a particular component in a codebase. If MTBF shows less time, it signifies some issues with the component which require immediate attention. This metric identifies components with significant issues, allowing the DevOps team to fulfill their primary goal of having less failure rate.

#Metrics 7: Deployment Success Rate:

The deployment success rate is the measurement of the number of successful and failed deployments by the DevOps team. The team can determine their deployment success rate through this DevOps efficiency metric. An efficient team will have a high deployment success rate. A team with a low rate needs to have an automated and standardized deployment process, allowing them to increase their deployment success rate.   

#Metrics 8: Availability and Uptime:

Every organization aims to attain its software’s utmost quality and speed, but downtime is an inevitable factor for an application. Getting to know about the availability and uptime of the software is a necessary DevOps productivity metric that will allow the DevOps team to plan maintenance. Measuring the acceptable downtime of an application is available, which can be measured through read-only and read or write availability. 

The goal of every DevOps team is to minimize downtime and increase the uptime of the software. If the team cannot maintain the balance between these two factors, they need to plan the downtime for maintenance. By taking this action, they foresee what can be done during that downtime and the actions necessary to reduce the outage. 

DevOps Key Performance Indicators(KPI’s):

Key performance indicators are sure signs or factors that should be monitored to analyze DevOps’ performance. With that in mind, here are all the primary DevOps KPIs that every organization and DevOps team should be aware of. 

#KPI 1: Feature Prioritization-

Every software comes with numerous features that can fulfill the everyday tasks of specific users. However, effective software has certain primary features which define the software. The DevOps team put in all their efforts to create new code to add new features to the software. Sometimes, the newly added or existing features may decline in usage. Keeping an eye on every feature’s usage will help the DevOps team prioritize the features and ensure that they always remain bug-free. If the team notices a reduction in usage of a particular feature, it is time to reassess the priorities and focus on features in demand by the users. Doing so will allow the DevOps team to enhance engagement and make the program more beneficial for the users. 

#KPI 2:Customer Ticket Volume-

Issues and bugs in software are inevitable but can be avoided too much extent by rigorous testing by the team. Sometimes, a few bugs can bypass all the tests and may reach the end consumer. In that case, the consumer will be reporting such issues to the developer, increasing the customer ticket volume. Having many new tickets indicates an underlying issue with the program that should be fixed immediately. Developers can use this KPI to find and fix bugs that were not identified during the testing stage.

#KPI 3:Defect Escape Rate-

As stated before, every software will have certain defects during its lifetime. An effective testing team will detect the issues during the testing or development stage of the pipeline. Specific bugs may go through this testing and may reach the direct consumers. Defect escape rate is the measurement of all such issues that bypass the testing phase and reach the end-user. A high defect escape rate indicates loopholes or inefficiency in testing by the DevOps team. A high rate team should optimize the testing protocols and increase the testing capabilities as well. 

#KPI 4: Unplanned Work-

As the name suggests, this Azure DevOps KPI is about analyzing the time spent by the DevOps team on unplanned works. To measure this KPI, the team must calculate the work aligned in the pipeline at the commencement of the DevOps cycle and compare the same with the work necessary to finish the release. Moreover, analyze the unplanned work done during that time and the ongoing progress in the process. 

If the developers are spending more than necessary time on unplanned work, it showcases the lack of stability or issues in the DevOps approach. Apart from that, inefficient testing or incapable test and production environments can also be the reason behind unplanned work. Spending too much time on such work will reduce the team’s productivity and compromise the overall software quality.

#KPI 6: Process Cycle Time-

Process cycle time is the overall time consumed by the DevOps team from the conceptualization stage to the final step of attaining feedback from the users. Using this DevOps flow metric, the team can calculate their software development cycle time. In general, the longer process cycle time signifies a lack of efficiency within the team and vice versa. However, a short cycle time should be achieved by compromising the code quality. The time consumed in a single project by a DevOps team should be justified. 

#KPI 7: Application Performance-

An application should perform well before and after deployment so that the user can make the most out of it. Post-testing the application, the DevOps team should analyze the application’s overall performance before final deployment. While analyzing the performance, the DevOps team can identify any hidden errors or underlying bugs, allowing the program to become more stable and efficient with its features. DevOps metrics tools can also be used in examining the application’s performance. 

Conclusion:

With all this information, now you have a better understanding of different DevOps CI/CD metrics and KPIs. Every DevOps team should utilize these key metrics and KPIs for the betterment of the team and the software so that they can enhance the software development life cycle. Without a doubt, there are dozens more DevOps KPIs and metrics, but calculating every factor is not an efficient way of working. Rather than doing everything, it is better to do what is best for the team and the organization. ThinkSys Inc will help your organization create the proper process for implementing DevOps KPIs and metrics. Our experts will understand your overall goals and your current and upcoming projects to provide you with an entirely customized roadmap for your DevOps. Furthermore, our team is proficient in using some of the industry-leading DevOps KPIs tools. 

Get Your Customized DevOps Roadmap Today

Frequently Asked Questions

A single DevOps metric cannot provide an accurate depiction of the performance. Several metrics should be used, and their collaborative result will be the right display of results. When it comes to measuring DevOps metrics, then multiple metrics, depending on the project and its requirements should be measured.

DevOps metrics provide a clear and unbiased overview of the DevOps software development pipeline’s performance, allowing the team to determine and eradicate issues. With these metrics, DevOps teams can identify their technical capabilities. Apart from that, these metrics help the teams assess their collaborative workflow, achieve a faster release cycle, and enhance the overall quality of the software.

DevOps KPI is a way to evaluate the performance of DevOps projects, practices, and products. Depending on the KPI, it provides in-depth information on the effectiveness of the DevOps team and project, along with the steps that should be taken to raise the quality standards.

Related Blogs:

  1. Software Development KPI’s and Metrics.
  2. Software Testing Metrics and KPI’s.
  3. Azure DevOps Pipeline Guide 2022.
  4. DevOps On Cloud.
  5. Multi-tenant Architecture For Cloud Apps.
DevOps on Cloud 2021

All You Must Know about DevOps on Cloud In 2022

DevOps has been referred to as the accelerated automation of agile methodology. The idea is to enable developers to meet real-time business requirements by releasing fast and iterating often. DevOps is the finely-tuned coming together of development, testing, and operations activities to eliminate any latency in software development procedures.

Of course, DevOps and Cloud Computing technology go hand in hand. The intense value of accelerated releases is best seen in cloud-based SaaS products where the changes can reflect immediately and updates can be rolled out instantly across all users.

DevOps on Cloud 2021

But the link between DevOps and cloud runs much deeper.

Cloud Computing provides centralized storage of the computing resources enabling DevOps automation with a centralized platform to carry out testing, deployment, and production activities. DevOps on the cloud resolves many of the concerns around distributed complexity. With such capabilities, a majority of the cloud computing vendors now provide DevOps support to enable continuous development and integration. Such easy integration brings down the costs associated with the on-premises DevOps platform. Centralized control is also enabled.

Benefits of DevOps on Cloud

Speed and agility are the primary benefits that businesses can experience with the synergy between DevOps and Cloud Computing. DevOps on the cloud covers all the application processes and life cycles beginning from code submission to its release. It enables a flexible choice of tools and products for effective capacity planning. It becomes possible to develop resources in a few minutes on the cloud, eliminating concerns around capacity expansion. The end-users get the ability to define infrastructure-as-code using declarative configuration files. These files can then be utilized to manage infrastructure resources, such as containers or virtual machines.

The following benefits are most commonly seen:

  • Enhanced pace of automation with reduced time to market
  • Effective cloud server replication
  • Real-time monitoring of services, such as backup services, management services, acknowledgment services, and others
  • Rapid deployment.

Controlling Cloud Costs:

It is challenging for organizations to control their respective cloud costs. Some of the reasons identified behind the inability to control these costs are ineffective analysis, complex public cloud offerings, poor cloud management, and a lack of transparency. With other measures to control costs, DevOps on the cloud can be an effective technique to control and manage cloud costs. DevOps involves holistic thinking wherein specific plans are developed for the entire environment including the budget and cost plans. These plans, being more comprehensive, provide a greater ability to control costs.

Key Points to Remember:

  1. Training on DevOps and Cloud: Integration of DevOps and cloud can bring along changes in the technical landscape and existing culture. Acceptance of the modified platforms and technologies can be made easy with training. Cloud and DevOps training becomes essential to explain to the individual the need for the technology changes and the requirements for DevOps on the Cloud.
  2. Security Consideration: Security models of organizations change irretrievably with cloud deployments. Robust security policies and controls must be extended to the DevOps platform when the two are integrated. Security must be synced with the continuous development and integration processes for better control and safety.
  3. DevOps Tools Selection: DevOps tools can be classified in different categories based on their availability and access, such as tools on-demand , on-premises, or ones as part of a large cloud platform. Many software organizations prefer to select DevOps tools and applications that can be deployed on multiple clouds. This helps improve the scalability and flexibility aspects of the organization.
  4. Service and Resource Governance: Governance is one aspect that often escapes due diligence. If that happens, the services, resources, or APIs inevitably become too complex to control and manage. Organizations must ensure that a governance infrastructure is in place and the policies around security, service management, resource management, and integration are defined in advance.
  5. Inclusion of Automated Performance Testing: Performance testing is a necessary inclusion within the automation testing suite in DevOps. It is important to carry out appropriate performance tests before production to ensure the improved quality of services at all times. The performance test cases must mesh with the accuracy, load, and stability tests along with the tests conducted to determine usability and API security.
  6. Consider Containers: Integrating containers in DevOps and cloud strategy can provide several benefits. Containers enable a mechanism to componentize applications to improve application portability and management. Effective utilization of the technology can provide better cluster management or security. A refined approach to application architecture needs to be adopted by organizations to achieve improved value and outcomes from DevOps on Cloud.

To Sum it Up:

DevOps on the cloud can provide a wide range of benefits to organizations. Of course, making this work involved factoring in several issues into the process. Aspects, such as training, tools selection, security, governance, containers, and performance testing must be considered to experience all the benefits of integrating DevOps with Cloud Computing technology. Once that is done, it could enable the creation of an unstoppable software development organization.

Get Your Free DevOps POC Here Today

Best CI/CD Practices

CICD Practices 2022

The world of software development has changed significantly over the past decade. Applications are everywhere. Mobile and web-based digital channels are the preferred routes for consumers. Expectations are rising on, what seems like, a daily basis. And that holds true for enterprise users as well as common folks.

Developers are increasingly under pressure to keep their codebases agile and open to extensions and upgrades always. Traditional modes of product, app, and solution delivery have found themselves turning to the DevOps methodology in search of ways to address ever-evolving customer needs. DevOps is helping bring much-needed flexibility and agility into practices that developers follow while building the digital assets today’s world demands.

CI/CD Best Practices

One foundation of DevOps relies on automating the deployment of new code versions for a digital offering. This automation has 2 critical categories into which activities fall:

#1. Continuous Integration (CI).

#2. Continuous Delivery (CD).

In simple terms, CI and CD are development principles that encourage automation across the process of an app development project. This empowers developers to make continuous changes in their code without disrupting the actual application that may be in use by end-users. Automation helps development teams deliver new functionalities faster in the product. This allows continuous product iteration.

In wake of the COVID 19 pandemic, software development teams across the world became more distributed than ever. For them, effective collaboration determines the efficiency of the software engineering process. In this scenario, CI and CD-led automation can also lead to better software quality and promote active collaboration between different teams working on a software project like Front-end, back-end, database, QA, etc.

Despite the benefits, several organizations are still not very confident in turning to CI and CD their deployments. A recent survey pointed out that only 38% of the 3650 respondents were using CI and CD in their DevOps implementations.

We believe that one of the key reasons for the slow adoption of CI and CD is the lack of awareness of what it takes to get CI/CD right. With that in mind, let us take a look at some of the best practices in CI/CD 2022 that every organization involved in developing digital applications must cultivate in their software engineering teams:

#1 : Treat CI and CD Individually:

While the end product requires a combination of CI and CD, the operational style for a DevOps-enabled project necessitates that development teams need to focus equally on CI and CD as two separate entities.

In CI, they can manage code changes that are smaller in size for either adding a new feature to an existing software product or making modifications or corrections of faults in the same. In CD, developers have to focus on transitioning their code from release to production through a series of automated steps that encompasses building and testing the code for readiness and finally sending it to end-user view.

CI may be easier to implement and companies can focus on moving ahead with CI first and then slowly set the pace for CD which encompasses testing, orchestration, configuration, provisioning, and a whole lot of nested steps.

#2: Design a Security-first Approach:

One of the key outcomes of implementing CI and CD is that organizations are equipped to make changes and roll out these changes to production on demand. At that accelerated pace, however, vulnerabilities may creep into the application due to confusion about roles and permissions.

Therefore, it is essential to bake security into the application at every step. Apart from focusing on the architecture and adopting a comprehensive safety posture, it is also essential to address the human element, often the weakest link in security.

As a best practice, people need to be assigned specific roles and permissions to be able to perform only what they are tasked to do and not access sensitive or confidential application components in production. Valuable deliverables can be protected by enabling role-based access control for staff who practice CI and CD regularly in their development activities.

#3: Create an enabling Ecosystem:

The technology leaders of organizations must make the effort of educating team members about the fact that CI and CD are part of holistic app development and delivery ecosystem and not a simple “input-output” process that can be linearly handled like in an assembly line.

Much is spoken about the need to create a culture of adherence to such practices. A key element of that culture is inculcating process discipline. DevOps, in general, and CI and CD, in particular, hold the potential to dramatically accelerate product delivery timelines. At that pace, alignment is super-critical. The people, processes, and tools must be brought into one page, roles defined, standards assured, and integrations meticulously planned to ensure that the activity moves forward with all stakeholders understanding and drawing value from the implementation.

#4: Improve with Feedback:

The fundamental objective app development teams seek to achieve with CI and CD is the ability to release fast and iterate often. This only makes sense when the product iterations, feature additions, and quality improvements are driven by the need to give the users what they need. Also, as with any software development paradigm, applications built with CI and CD can be susceptible to incidents, defects, and issues in their lifecycle.

Therefore, it is important for app development teams to build processes that allow them to capture user feedback, work it into the product (or app), test it for its ability to deliver value to the users, and release it fast. Teams must gain feedback, identify patterns through retrospective analysis, and use this learning to improve future CI and CD deployments.

Conclusion:

CI and CD open the doors to higher-quality software. Organizations that leverage CI/CD best practices and concepts will gain the ability to differentiate their digital assets from the competition. With faster time to market and lower defects guaranteed, CI and CD help create a development ecosystem suited for high-end products needed by the consumers of today.

Get Free CI/CD Suggestions From our Experts

A Long Hard Look at AIOps

A Long Hard Look at AIOps

AIOps or Artificial Intelligence for IT operations means applying artificial intelligence (AI) to improve IT operational effectiveness. AIOps makes use of aspects like analytics, big data, and machine learning abilities to perform its functions like –

  • Gathering and aggregating large and ever-increasing amounts of operations data created by several IT infrastructure components, performance-monitoring tools, and applications.
  • Intelligently zeroing in on the ‘signals’ in all that ‘noise’ to categorize important patterns and events associated with the availability issues and system performance.
  • Diagnosing root causes and reporting them to the IT section for swift response and recovery actions. In some cases, it helps to resolve these issues automatically without any need for human intervention.
  • Enabling IT operations teams to react rapidly by replacing several individual, manual IT operations tools with one intelligent and automated IT operations platform. It also helps to avoid slowdowns and outages proactively, without effort.

Many experts believe that AIOps will become the future of overall IT operations management.

 

A Long Hard Look at AIOps

The Need for AIOps

Nowadays, several organizations are abandoning the traditional infrastructure consisting of individual, static physical systems. Today, it’s all about a dynamic combination of on-premise, managed, private, and public cloud settings. They prefer running on virtualized or software-oriented resources that upgrade and reconfigure continually.

Various systems and applications across these environments create an ever-rising tidal wave of operational data. The average enterprise IT infrastructure, as estimated by Gartner, produces three-times extra IT operations data annually.

Traditional domain-based IT management solutions can be brought to their knees by volume of data. Intelligently sorting the important events out of the mountain of data is a dream, at best. Correlating data through various but interdependent environments is out of the question. Adding to that, providing predictive analysis and real-time insight for IT operations teams and enabling them to respond to issues promptly, becomes unrealistic. Then, we could wave goodbye to meeting user and customer service level expectations.

With AIOps, you can secure deep visibility into data performance and dependencies through various environments through a unifying solution. You can analyze the data and parse out significant events associated with outages or slowdowns. It can automatically alert IT staff to the issues, their origin and suggest actionable solutions.

 

How does AIOps work?

The easiest way to understand the working of AIOps is by reviewing the role played by each AIOps component. It includes machine learning, big data, and automation in the operational process.

AIOps makes use of big data platforms to combine siloed IT operations data. This includes:

  • System logs and metrics
  • Historical performance and event data
  • Streaming real-time operations events
  • Incident-related data and ticketing
  • Network data, including packet data
  • Related document-based data

AIOps then taps focused on machine learning and analytics capabilities.

  • Individual important event alerts from the ‘noise’: AIOps applies analytics like pattern matching and rule application to sift through the IT operations data and individual signals that denote any important anomalous event alerts.
  • Recognize the origin of the issues and suggest solutions: By utilizing environment-specific or industry-specific algorithms, AIOps can compare abnormal events with other event data from all the environments to pinpoint the reason for any performance or outage problem and propose apt remedies.
  • Automate responses together with actual proactive resolution: AIOps can route alerts automatically and suggest solutions to the right IT teams. It can also generate response teams depending on the problem’s nature and the solution. In several instances, it can process the results from machine learning to activate automatic system responses. It can address the problems happening in real-time, even before the users become aware of their occurrence.
  • Learn always to improve future managing problems: Depending on the machine learning capabilities, analytics AIOps can alter algorithms or develop new ones to recognize problems before occurrence and propose practical solutions. AI models can also support the system to learn about and become accustomed to environment changes, like a new infrastructure installed or reconfigured by DevOps.

Benefits of AIOps

The all-encompassing benefit of AIOps is that it allows IT operations to detect, address, and resolve outages and slowdowns quicker than manually through alerts from several IT operations tools. It results in quite a few benefits, such as –

  • Attain faster mean time to resolution (MTTR): AIOps can identify the root causes of problems earlier and more precisely than humanly possible. It helps the organizations to fix and attain ambitious MTTR goals. For instance, Nextel Brazil, a telecommunications service provider, could minimize incident response times from 30 minutes to 5 minutes with AIOps.
  • Moving from responsive to proactive to prognostic management: AIOps keeps on learning and better detects less-urgent signals or alerts as opposed to more-urgent circumstances. It can offer predictive alerts that allow the IT teams to address impending problems before they cause outages or slowdowns.
  • Streamline IT operations and IT teams: As an alternative to being buried under every alert from every environment setting, only alerts that meet particular service level thresholds or parameters can be sent to AIOps operations teams. It carries the full context necessary for the team to decide on the best possible diagnosis and carry out the fastest corrective measure. As AIOps keeps on learning, improving, and automating, it results in more efficiency with less human effort. Your IT operations team can concentrate on tasks that bring immense strategic value to the business.

AIOps Use-Cases

On top of optimizing IT operations, the visibility and automation support offered by AIOps can help drive other vital aspects of business and IT initiatives. Some of its use cases are as follows –

  • Digital transformation: AIOps is designed to handle complex digital transformation in IT operations. It encompasses virtualized resources, multiple environments, and dynamic infrastructure. This enables freedom and flexibility.
  • Cloud adoption or migration: Cloud adoption is a gradual process. The norm is a hybrid and multi-cloud setup with several interdependencies that can alter too frequently and quickly to document. AIOps can radically decrease the operational risks by offering a clear vision of the interdependencies in cloud migration in such situations.
  • DevOps adoption: DevOps drives development forward by offering more power to setting up and reconfiguring infrastructure for the development teams. However, IT still has to tackle that infrastructure. AIOps offers the necessary automation support to DevOps for effortless management.

AIOps promises to decouple organizational ambitions from the management headache imposed by ballooning IT Infrastructure. This intelligent, automated, and optimized approach to managing the IT backbone could well become an enterprise technology mainstay soon.

Get AIOps Suggestions From our Experts

Categories