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.

• 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:

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.

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.  

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. 

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Frequently Asked Questions

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.

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.

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.

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It’s reasonable to assume that DevOps has two parallel, and equally important, objectives. One primary aim of DevOps is to reduce the development lifecycle with continuous delivery of software to the clients and end-users and the other crucial objective is to improve the software quality. 

It’s never been up for debate that testing is an extremely critical phase in software development. Now, with transformations in the development cycles with fast-paced approaches, such as DevOps and Agile, how we look at testing has evolved. It is now essential to implement smart ways of testing software products and applications. Test automation is one of the approaches to improve testing speed and accuracy. 

DevOps Testing Strategy 

Before moving to the test automation mechanisms in the world of DevOps, it is necessary to make a pitstop to examine the factors feeding into the DevOps testing strategy. DevOps supports and includes a continuous testing strategy which means testing is conducted at every phase of the process. Testers are involved in the testing of the development plan, design testing, and operations testing with functional and non-functional testing. For example, risk-based or exploratory testing can be executed to test the software designs. When it comes to releasing, the combination of tests can run on the production and test environments.

The primary idea in the DevOps testing strategy is to continuously look for possible gaps and errors. DevOps involves testing right from the initiation till the very end.

Test Automation and DevOps

As stated earlier, DevOps supports and follows a continuous testing strategy. Also, continuous development and delivery are involved in DevOps. At that pace, a high level of collaboration and fast-paced execution is required to meet the expected efficiency and quality levels. 

This is where test automation becomes the key to support the DevOps practices and make sure software quality is always maintained and improved. Some of the best practices for beginning test automation include: 

• Begin with the test automation flows that are easy and increase the complexity and coverage over time.
• Develop independent and self-contained automation test cases.
• Maintain collective ownership during test automation.
• Collaborate with design, development, and deployment teams.

While following the practices illustrated above, the Test Automation Engineers may still get confused about the mechanisms to integrate these with DevOps. A common workflow is presented below to enable test automation teams to amalgamate automation testing in DevOps practices. 

• The test engineers shall meet with the developers to discuss the user story and list down the behaviors from a business standpoint. The behaviors identified shall then be converted to Behavior-driven development (BDD) tests. 
• Developers shall work on the user story and create unit and integration tests in collaboration with the testing team under test-driven development (TDD). The shared code repository shall be set-up and the tests and codes must be deployed in the repository. 
• DevOps Engineers shall create Continuous Integration (CI) servers to execute the code in the shared repository and execute all the tests in TDD and BDD. 
• Automation Engineers shall analyze these workflows and tests to create the automated test scripts. The engineers shall also develop additional tests around performance, security, and non-functional testing. 
• DevOps Engineers shall reuse the test scrips loaded in the shared repository for acceptance testing.

DevOps’ continuous testing strategy involves several resources and the Automation Test Engineers must collaborate with these resources to effectively conduct test automation.

DevOps Test Automation Tools 

Obviously, there are many test automation tools available in the market, and making the right choice is complex. To conduct test automation in DevOps, the tool selected must have the following features: 

• Seamless integration in the CI/CD pipeline. 
• Platform-independence to run in any of the infrastructures. 
• Multi-user access to be used by testers, developers, and others at the same time. 
• The short learning curve for better release management. 
• Maintenance of automation tests and scripts.
• Multiple language options – JavaScript, PowerShell, C#, etc.

Each tool will come with a set of features and benefits that will decide its aptness for each specific situation. For instance, TestComplete is a typical automation tool that can meet some test automation requirements in DevOps. This is an automated UI testing tool that can support a variety of test cases with enhanced test coverage. The tool comes with record and replay capabilities and an AI-equipped customizable object repository. Tools like these can allow automation test engineers to develop end-to-end tests quickly and efficiently. Good test automation tools can be easily integrated with the various continuous integration systems. Given the prevailing environment with remote teams, it’s also useful to look if the tool comes with distributed testing capabilities. The right set of features will help enhance the testing abilities of the team and also simplify the maintenance tasks. 

The bar for software quality has been raised very high and the consequences for failing this test can be dire for a product or application. In the uber-accelerated world of DevOps, software testing has to take on a completely new dimension. Given the need to test more, test faster, and test better, automation presents itself as the most appropriate strategy to achieve software quality. 

Is your Software Testing Strategy DevOps Ready?

Visualize this: In the coming two years, more than 500 million new applications will be built — a number equal to total applications developed in the last four decades. 

This explosion in applications will be the result of businesses’ efforts to turn into “digital innovation factories”. Intrinsically, businesses will create digital products and services with speed and scale that will be at the heart of their digital value proposition. And a number of these applications will be built and deployed in containers. 

Container-powered infrastructure is pulling enormous interest world-wide because containers enable agile and automated deployment of modern applications at scale and economy. A single server can host several containers as compared to virtual machines (VMs) for higher utilization. Considering the speed, efficiency, and practicality of containers in managing cloud-native applications, businesses are adopting containers at never before rates.

Here are five things that you must know about Containers:

#1: Containers Enhance Continuous Integration (CI) and Continuous Delivery (CD) Processes–

The advancement in continuous integration and continuous delivery processes has enabled developers to implement and deliver applications rapidly and frequently. Containers drive CI/CD advantages further via portability. When each container can be seamlessly and dependably moved to different platforms, like between a developer’s device and a private/public cloud, CI/CD processes become seamless. 

Containers can also be replicated or scaled without suspending other processes, and each container’s individuality enables applications to be developed, tested, deployed, and modified simultaneously, thereby eliminating interruptions and delays. By utilizing containers combined with CI/CD, the entire software delivery life cycle (SDLC) speeds up, with lesser manual tasks, and challenges of migrating between different environments. 

#2: Containers Refashion Legacy Applications –

Most businesses don’t have the luxury to build “all-new” applications for cloud-based platforms. Rather they prefer migrating existing or legacy applications to the cloud. Many applications can utilize the ‘lift and shift’ approach to the cloud, signifying that most will need to be radically refactored to benefit from the cloud features as code alterations are made. The applications are revamped, recoded, and repurposed for cloud platforms giving the application – a new purpose. 
This is not easy, and there are innovative technologies that need to be considered. Applications are enabled to externalize APIs, and microservices allow applications to leverage the best functionality on cloud platforms. Containerization of the applications guarantees a seamless distributed architecture and cloud-to-cloud portability. 
Containerizing legacy applications comes with several benefits, such as reducing complexity by utilizing container abstractions. The containers eliminate the dependencies on the underlying infrastructure services, which further lessens the complications of dealing with those platforms. This implies that developers can abstract the access to resources, like storage, from the application itself. This makes the application portable, but at the same time also speeds the refactoring of the applications.

#3: Containers Create Dependable and Resilient Environments –

With the help of Kubernetes, containers can either operate on the same server and utilize similar resources or can even be distributed. Individual containers allow the parallel development of applications and ensure that a break down in one application does not disturb or cause a failure in other containers. This isolation also enables teams to quickly detect and fix technical problems without triggering any downtime in other areas.

Containers offer the best of both worlds, enabling resource sharing while reducing downtime and permitting teams to prolong developing innovative functions. The result is highly-efficient environments that enable teams to march forward with software development and delivery, although other teams are caught up testing or fixing errors.

#4: Containers – A Better Option for Virtualization –

In the conventional approach of virtualization, a hypervisor virtualizes physical hardware. Every virtual machine holds a guest OS, a computer-generated copy of the hardware that the OS needs to stream, and an application and its related libraries and dependencies.
Rather than virtualizing the fundamental hardware, containers virtualize the operating system (usually Linux), so every independent container encompasses only the application along with its libraries and dependencies. Containers are slim, speedy, and portable because, as opposed to virtual machines, containers don’t require a guest OS in every instance and can utilize the features and resources of the host OS.
Just like virtual machines, containers enable developers to enhance CPU and memory utilization. However, containers go a step further because they also power microservice architectures, where application components can be employed and scaled more minutely. This is a lucrative option to scale up a monolithic application because a single component takes the load.

#5: Containers Offer Superior Performance:

The slashed resource load is a key reason for businesses to leverage containerized platforms over virtual machines. Containers provide more than ten times the density suggesting that developers can operate up to ten times more containers in a single host.  

Additionally, hypervisors are susceptible to latency issues. As compared to virtual machines, containers considerably reduce latency. Furthermore, containers load much faster than virtual machines. Containers thus offer a substantial boost in performance by decreasing the resource load and latency. And the quicker load time caters to a seamless user experience.

Containers will continue to grab market share from conventional virtualization technologies. This technology is already fast-tracking digital transformation and application modernization efforts for several businesses and across diverse applications. We may not physically see containers being utilized, but the truth be told, we utilize them every day. Be it Google or Netflix, we are using containers every day in the back end. 

The adoption of containers is real and is revolutionizing how businesses are deploying IT infrastructure. From rapidly delivering applications to amplifying development to deployment processes, to slashing infrastructure and software costs, containers offer brilliant business outcomes to application developers. 

First, there were developers. And then software development got more challenging, more complex, less straightforward. That resulted in the emergence of a new “combo” discipline – DevOps. 

DevOps was seen as a medium for fuelling software teams into supercharged IT powerhouses. -Click Here to Tweet

DevOps was introduced to improve collaboration. It is a working culture that smashes the conventional siloes between software development, quality assurance, and operations teams, empowering all application life-cycle stakeholders to work collectively – from conception to design, development, production, and support.

But all is not what it seems in the world of DevOps. DevOps puts pressure on teams to deliver faster releases while scaling with demand. On this path, the cloud is one of the significant resources needed to make a DevOps environment run smoothly. And this is where the challenge lies.

Where Does The Cloud Come Into Picture?

DevOps fast-tracks the growth in cloud infrastructure needs far beyond what conventional application development methods may have required. As the organization shifts from monthly to daily releases, the infra needs keep scaling, often in an unplanned manner.

If DevOps is the most significant transformation in the IT process in decades, renting infrastructure on demand was the most disruptive transformation in IT operations. With the change from traditional data centers to the public cloud, infrastructure is now leveraged like a utility. Like any other utility, there is a waste here too. (Think: leaving the fans on or your lights on when you are not home.)

The extra cloud costs encompass several interrelated problems: ongoing services when they do not need to be, wrongly sized infrastructure, orphaned resources, and shadow IT. People leveraging AWS, Azure, and Google Cloud Platform are either already feeling the pressure — or soon will. Since DevOps teams are primarily cloud users in many organizations, DevOps cloud cost control processes must become a priority in every organization.

Why Is It So Challenging For Organizations To Get Their Cloud Costs Under Control? -Click Here to Tweet

In an excellent analysis on CIO.com, the following three challenges were highlighted :

1. Playing too safe with Cloud Provisioning:

   During most of the primary generations of public cloud initiatives, the goal of the DevOps team was the development speed and quality of the solution. In the standard three-way trade-off of products, organizations can accomplish two of three goals – speed, quality, and low-cost – but not all three. Often, low cost has been the odd-man-out. With a “better-safe-than-sorry” attitude, several DevOps teams habitually purchased more cloud capacity and functionality than their solutions needed. More capacity means more cost.

2.  Complex public cloud offerings:

   As public cloud platforms like AWS and Microsoft Azure are maturing, their portfolios of service options have radically grown. For example, AWS catalogs roughly 150 products grouped under 20 categories (compute, database, developer tools, AI, analytics, storage, and so forth). This sort of portfolio makes for roughly a million distinct potential service configurations. Incorporate frequent price changes for services and picking the best and most cost-effective public cloud options make assessing cell-phone plans look like child’s play. More complexity often means poor choices that drive higher costs.

3. Lack of transparency and effective analysis:

   Organizations don’t have good visibility into how much infrastructure their cloud apps require to provide the necessary functionality and service levels. Without tools that provide such analysis, organizations can’t pick the best options, right-size existing public cloud deployments, or eliminate “deadwood” cloud apps that never got eliminated as DevOps teams moved on to create new cloud solutions. It’s time for organizations to get serious about optimizing and controlling their use of cloud resources and – in so doing – cutting unnecessary public cloud costs. To do this, they must utilize analytics tools and services that can offer actionable data about their cloud deployments and aid them to traverse through the jungle of public cloud service and pricing options.

The Cultural Behavior of Controlled Costs

While Continuous Cost Control is an idea that organizations must apply to development and operations practices right through all project phases, organizations can do a few things to begin a cultural behavior of controlled costs. Build a mindset and apply the principles of DevOps to control cloud costs.

• Holistic Thinking: In DevOps, organizations need to think about the environment as a whole. Organizations have budgets. Technology teams have budgets. Whether you care or not, that also implies that DevOps has a budget, it needs to stay within. At some point, the infrastructure cost must come under scrutiny.
• No silos: No silos imply not only no communication silos but also no silos of access. This applies to cloud cost control when it comes to challenges such as absconding compute instances running when they’re not required. If only one person in the organization possesses the ability to turn instances on and off, then that’s an undesirable single point of failure.
  The solution is removing the control silo by enabling users to access their instances to turn them on as and when they require them, utilizing governance via user roles and policies to make sure that cost control strategies remain uninhibited.
• Quick and Valuable Feedback: In eradicating cloud waste, the feedback required is – where is waste occurring? Are your instances appropriately sized? Are they functioning when they don’t need to be? Are there orphaned resources eating the budget?
  Valuable feedback can also come in total cost savings, percentages of time instances were shut down over the previous month, and overall coverage of your cost optimization efforts.  Reporting on what is working helps organizations choose how to address the challenges. Organizations need monitoring tools to discover the answers to these questions.

Following this cultural behavior shift, DevOps teams can transition from preserving, archiving, and destroying data to collecting and utilizing it for data-driven insights. This transformation in mindset toward cloud removes constraints and enables to innovate faster and more susainably.

Act Now

Inspect your DevOps processes today and see how you can integrate a DevOps cloud cost control mindset. Consider automating cost control to lessen your cloud expenses and make your CFO’s life happier.

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Offshore software development has never been easy. Neither has DevOps. Although both offer a distinct set of advantages to organizations, trying to do them together could be challenging. In addition to creating a culture of collaboration, new tools have to be adopted. Yet, many large global organizations have successfully built DevOps capabilities across time zones, while meeting requirements 24×7 – within time and budget. 

Here’s how Offshore Development has Changed with DevOps:

1. The Improvement in Product Quality: Quality management has always been a basic requirement of software development, and also a popular way to control development costs. But with offshore development, quality management gained a reputation for being rigid and imbalanced. Offshore teams had a tough time balancing quality and costs. The perception grew that they could only focus on one aspect while overlooking the other. However, DevOps brings in a way for offshore development teams to drive quality and costs simultaneously. Since there is more collaboration between teams, bugs are identified quickly – which improves quality, and there is less rework – which reduces the associated costs. 
2. The Stress on Culture: Offshore development teams have often focused on the tools and technologies needed to drive outcomes. However, with the advent of DevOps, there is a ton of business culture aspects to consider. When DevOps comes into the picture, it’s not just about tooling; teams have to work together and collaborate to drive the intended DevOps outcomes. Rather than looking at culture as a nice-to-have feature, offshore development teams have started to look at it as a core competency that lays the foundation of an efficient software development practice. 
3. Accelerated time-to-Market: Since the dawn of offshore development, teams have been following the sun; once early analysis and design are complete, documentation is sent to remote developers to start coding and testing. However, what DevOps does, is turn all of this on its head; by seeking greater collaboration between teams, it helps them release software in bite-sized sprints – so teams can get more frequent visibility and feedback. Such an approach builds faster feedback loops, accelerates the velocity at which a company can test hypotheses about what the client wants – without wasted time and effort – and brings products to market sooner. 
4. The Elimination of Hand-offs: Offshore development has also always been about hand-offs. When one person (or team) is done with a piece of work, a key milestone is achieved, and he/she then notifies the other to start working. However, what DevOps does is just the exact opposite. It enables different teams to work on aspects of software development in tandem, while greatly reducing the number of handoffs or delays. Teams do not have to waste time waiting for a “go-ahead” to start working; instead, they drive continuous collaboration through the entire development life cycle, keep track of tasks across coding, unit testing, build scripts, configuration scripts and avoid passing work back and forth. 
5. The Growth of Analytical Dashboards: For offshore teams having a tough time getting visibility into project status, DevOps drives the use of analytical dashboards. These dashboards often serve the purpose of providing a single source of truth across the complete organization, while giving real-time updates on project status, issues, challenges, and improvement opportunities. Teams that leverage these tools find themselves resolving issues faster while making the entire process of offshore development far more effective.
6. Handling out-of-Scope Requests: Offshore teams have always found it difficult to handle out-of-scope requests and cater to emergency patch-up works which come out of their schedule. This is mainly due to the differences in time zone. However, with DevOps, the project’s scope is clearly defined through several iterations of communication between the internal team and the offshore team. Any out-of-scope request can be accommodated, based on the availability of resources, as can urgent jobs which need immediate attention.  

Improve Software Development Outcomes: 

When the world embraced the offshore development model, the productivity gains and cost savings stimulated technological innovation for years to come. While offshoring helped businesses achieve their market and customer goals – quickly and more efficiently, it also paved the way for the adoption of methodologies and approaches to produce software more efficiently and effectively. 

DevOps is one such transformation, that is helping offshore teams break departmental siloes, and drive a cultural shift towards efficient software delivery. The changes range from dramatically improving software quality to accelerating time-to-market, eliminating wasteful hand-offs, to offering real-time visibility into product status while seamlessly handling out-of-scope requests. The impact of DevOps on offshoring has been phenomenal, and the approach will continue to boost offshore development outcomes for years to come.

As the requirement for high-quality software in short time frames and restricted budgets increases, developers are looking for approaches that make building software a lot faster and more efficient. DevOps greatly helps in improving the software product delivery process; by bridging the gap between the development and operations teams, DevOps facilitates greater communication and collaboration, and improves service delivery, while reducing errors and improving quality. According to the State of Agile report, 58% of organizations embrace DevOps to accelerate delivery speed.

Tools for a successful DevOps Strategy

DevOps creates a stable operating environment and enables rapid software delivery through quick development cycles – all while optimizing resources and costs. However, before you embark on the DevOps journey, it is important to understand that since DevOps integrates people, processes, and tools together, more than tools and technology, it requires a focus on people and organizational change. Begin by driving an enterprise-wide movement – right from the top-level management down to the entry-level staff – and ensure everyone is informed of the value DevOps brings to the organization before integrating them together into cross-functional teams.

Next, selecting the right tools is critical to the success of your DevOps strategy; make sure the tools you select work with the cloud, support network, and IT resources and comply with the necessary security and governance requirements. Here’s your 5-point guide for developing a successful DevOps strategy and the tools you would need to drive sufficient value:

1. Understand your Requirements: Although this would seem a logical first step, many organizations often make the DevOps plunge in haste, without sufficient planning. Start by understanding the solution patterns of the applications you plan to build. Consider all important aspects of software development including security, performance, testing, and monitoring — basically all of the core details. Use tools like Pencil, a robust prototyping platform, to gather requirements and create mockups. With hundreds of built-in shape collections, you can simplify drawing operations and enable easy GUI prototyping.
2. Define your DevOps Process: Implementing a DevOps strategy might be the ideal thing to do, but understanding what processes you want to employ and what end result you are looking to achieve is equally important. Since DevOps processes differ from organization to organization, it is important to understand which traditional approaches to development and operations to let go of as you move to DevOps. Tools like GitHub can enable you to improve development efficiency and enjoy flexible deployment options, centralized permissions, innumerable integrations and more. GitHub allows you to host and review code, manage projects, and build quality software – moving ideas forward and learning all along the way.
3. Fuel Collaboration: Collaboration is a key element of any DevOps strategy. It is only through continuous collaboration that you can develop and review code and stay abreast with all the happenings. With frequent and efficient collaboration, you can efficiently share workloads, enable frequent reviews, be informed of every update, resolve simple conflicts with ease, and improve the quality of your code. Collaboration tools like Jira and Asana enable you to plan and manage tasks with your team across the software development lifecycle. While Jira allows team members to effectively plan and distribute tasks, prioritize and discuss team’s work, and build and release great software together, Asana allows project leaders to assign responsibilities throughout the project; you can prioritize tasks, assign timelines, view individual dashboards and communicate on project goals.
4. Enable Automated Testing: When developing a DevOps strategy, it is important to enable automated testing. Automated test scripts speed up the process of testing, and also improve the quality of your software by testing it thoroughly at each stage. By leveraging real-world data, they reflect production-level loads and identify issues in time. DevOps-friendly tools like Selenium are ideal for enabling automated testing. Since Selenium supports multiple operating systems and browsers, you can write test scripts in various languages including Java, Python, Ruby and more and can also extend test capability using additional test libraries.
5. Continuously Monitor Performance: To get the most out of your DevOps strategy, measuring and monitoring performance is key. Given the fact that there will be hundreds of services and processes running in your DevOps environment, all of which cannot be monitored, the identification of the key metrics you want to track is vital. Tools like Jenkins can be used to continuously monitor your development cycles, deployment accuracy, system vulnerabilities, server health, and application performance. By quickly identifying problems, it enables you to integrate project changes more easily and deliver a functional product more quickly.

Improve Service Delivery

Implementing a DevOps strategy is not just about building high-quality software faster; it’s about driving a cultural shift across the organization to improve development processes and make it more efficient. Making the most of a switch to DevOps requires you to start with a new outlook, along with the use of new tools and new processes. By using the right tools at every stage, you can accelerate the product development process, meet time-to-market deadlines, and begin your journey towards improved service delivery and optimized costs.

The past few years have witnessed the meteoric rise of DevOps in the software development landscape. The conversation is now shifting from “What is DevOps” to “How can I adopt DevOps”. That said, the Puppet’s State of DevOps Report stated that high performing DevOps teams could deploy code 100 times faster, fail three times less and recover 24 times faster than the low performing teams. This suggests that DevOps, like with every other change in the organization, can be beneficial only when done right. In the haste to jump on the DevOps bandwagon, organizations can forget that DevOps is not merely a practice but is a culture change – a culture that breeds success based on collaboration. While DevOps is about collaboration between teams, continuous development, testing and deployment, key mistakes can lead to DevOps failure. Here’s a look at some common DevOps mistakes and how to avoid them.

1. Oversimplification:
   DevOps is a complex methodology. In order to implement DevOps, organizations often go on a DevOps Engineer hiring spree or create a new, often isolated, DevOps department to manage the DevOps framework and strategy. This unnecessarily adds new processes, often lengthy and complicated. Instead of creating a separate DevOps department, organizations must focus on optimizing their processes to create operational products leveraging the right set of resources. For successful DevOps implementation, organizations must manage the DevOps frameworks, leveraging operational experts and other resources that will manage DevOps related tasks such as resource management, budgeting, goals and progress tracking.
   DevOps demands a cultural overhaul and organizations should consider a phased and measured transition to DevOps implementation by training and educating employees on these new processes and have the right frameworks in place to enable careful collaboration.
2. Rigid DevOps processes:
   While compliance with core DevOps tenets is essential for DevOps success, organizations have to proactively make intelligent adjustments in response to enterprise demands. Organizations thus have to ensure that while the main DevOps pillars remain stable during DevOps implementation, they make the internal adjustments that are needed in internal benchmarking of the expected outcomes. Instrumenting codebases in a granular manner and making them more partitioned gives more flexibility and gives DevOps teams the power to backtrack and identify the root cause of diversion in the event of failed outcomes. However, all adjustments have to be made while remaining within the boundaries defined by DevOps.
3. Not using purposeful automation:
   DevOps needs organizations to adopt purposeful automation – automation that is not done in silos like change management or incident management. For DevOps, you must adopt automation across the complete development lifecycle. This includes continuous delivery, continuous integration, and deployment for velocity and quality outcomes. Purposeful end-to-end automation is essential for DevOps success. Hence organizations must look at complete automation of the CI and CD pipeline. At the same time, organizations need to keep their eyes open to identify opportunities for automation across processes and functions. This helps to reduce the need for manual handoffs for difficult integrations that need additional management and also in multiple format deployments.
4. Favoring feature-based development over trunk-based development:
   Both feature-based development and trunk-based development are collaborative workflows. However, feature-based development, a development style that provides individual features their isolated sandboxes, adds to DevOps complexity. As DevOps automates many aspects of the code between development and production environments, keeping different conceptual flavors around the codebase makes DevOps more complex. Trunk-based development, on the other hand, allows developers to work in a coherent and single version of the codebase and alleviates this problem by giving developers the capability to manage features through selective deployments instead of through version control.
5. Poor test environments:
   For DevOps success, organizations have to keep the test and production environments separate from one another. However, test environments must resemble the production infrastructure as close as possible. DevOps means that testing starts early in the development process. This means ensuring that test environments are set up in different hosting and provider accounts than what you use in production. Testing teams also have to simulate the production environment as closely as possible as applications perform differently on local machines and during production.
6. Incorrect architecture evaluation:
   DevOps needs the right architectural support. The idea of DevOps is to reduce the time spent on deploying applications. Even when automated, if deployment takes longer than usual there is no value in the automation. Thus, DevOps teams have to pay close attention to the architecture. Ensure that the architecture is loosely coupled to give developers the freedom and flexibility to deploy parts of the system independently so that the system does not break.
7. Incorrect incident management:
   Even in the event of an imperfect process, DevOps teams must have robust incident management processes in place. Incident management has to be a proactive and ongoing process. This means that having a documented incident management process is imperative to define incident responses. For example, a total downtime event will have a different response workflow in comparison to a minor latency blip. The failure to do so can lead to missed timelines and avoidable project delays.
8. Incorrect metrics to measure project success:
   DevOps brings the promise of faster delivery. However, if that acceleration comes at the cost of quality then the DevOps program is a failure. Organizations looking at deploying DevOps thus must use the right metrics to understand progress and project success. Therefore, it is essential to consider metrics that align velocity with throughput success. Focusing on the right metrics is also important to drive intelligent automation decisions.

To drive, develop, and sustain DevOps success, organizations must focus on not just driving collaboration across teams but also on shifting the teams’ mindset culturally. With a learning mindset, failure is leveraged as an opportunity to learn and further evolve the processes to ensure DevOps success.

The path to building cutting-edge software solutions is often paved with several obstacles. Disjointed functioning of various development teams often results in long release cycles. This not only results in a poor quality product but also adds to the overall cost of development. For organizations looking to set themselves apart from the competition, it has become essential to embrace the world of DevOps and enable frequent delivery of good-quality software.

The Growth of DevOps

Conventional software development and delivery methods are rapidly becoming obsolete. Since the software development process is a long and complex one, the process requires teams to collaborate and innovate with each passing day. Models evolved -first it was Waterfall, then Agile, and now it’s DevOps – in order to meet the dynamic demands of the industry, and growing expectations of the tech-savvy user, the software development landscape is undergoing constant change. Today, DevOps is being seen as the most efficient method for software development. According to the recently released 2017 State of DevOps Report, high performing organizations that effectively utilize DevOps principles achieve 46x more frequent software deployments than their competitors, 96x faster recovery from failures, and 440x faster lead time for changes. There seems little room for doubt any longer about the impact of DevOps.

DevOps aims at integrating the development and operations teams to enable rapid software delivery. By fuelling better communications and collaboration, it helps to shorten development cycles, increase deployment frequency, and meet business needs in the best possible manner. Using DevOps, software organizations can reduce development complexity, detect and resolve issues faster, and continuously deliver high-quality, innovative software. The two pillars of successful DevOps practice are continuous integration and continuous delivery. So, what are these terms? What do they mean? And how do they help in meeting the growing demands of the software product industry? Let’s find out!

Continuous Integration

Definition: Continuous Integration (CI) aims at integrating the work products of individual developers into a central repository early and frequently. When done several times a day, CI ensures early detection of integration bugs. This, in turn, results in better collaboration between teams, and eventually a better-quality product.

Goal: The goal of CI is to make the process of integration a simple, easily-repeatable, and everyday development task to reduce overall build costs and reveal defects early in the cycle. It gets developers to carry out integration sooner and more frequently, rather than at one shot in the end. Since in practice, a developer will often discover integration challenges between new and existing code only at the time of integration, if done early and often, conflicts will be easier to identify and less costly to solve.

Process: With CI, developers frequently integrate their code into a common repository. Rather than building features in isolation and submitting each of them at the end of the cycle, they continuously build software work products several times on any given day. Every time the code is inputted, the system starts the compilation process, runs unit tests and other quality-related checks as needed.

Dependencies: CI relies heavily on test suites and an automated test execution. When done correctly, it enables developers to perform frequent and iterative builds, and deal with bugs early in the lifecycle.

Continuous Delivery

Definition: Continuous Delivery (CD) aims to automate the software delivery process to enable easy and assured deployments into production —at any time. By using an automatic or manual trigger, CD ensures the frequent release of bug-free software into the production environment and hence into the hands of the customers.

Goal: The main goal of CD is to produce software in short cycles so that new features and changes can be quickly, safely, and reliably released at any time. Since CD involves automating each of the steps for build delivery, it minimizes the friction points that are inherent in the deployment or release processes and ensures safe code release can be done at any moment.

Process: CD executes a progressive set of test suites against every build and alerts the development team in case of a failure, which then rectifies it. In situations where there are no issues, CD conducts tests in a sequential manner. The end result is a build that is deployable and verifiable in an actual production environment.

Dependencies: Since CD aims at building, testing, and releasing software quickly and frequently, it depends on an automated system that helps the development team to automate the testing and deployment processes. This is to ensure the code is always in a deployable state.

CI/CD for Continued Success

Software development involves a high degree of complexity that requires teams to embrace new and modern development methodologies in order to meet the needs of business and end-users alike. DevOps focuses on the continuous delivery of software through the adoption of agile, lean practices. The pillars of DevOps, CI, and CD, improve collaboration between operations and development teams and enable the delivery of high-quality software for continued success. RightScale estimates that over 84% of organizations have adopted some aspect of DevOps principles -it’s time you do too. As DevOps pundit Jez Humble rightly says, “DevOps is not a goal, but a never-ending process of continual improvement”.