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Your 2022 Continuous DevOps Monitoring Solution Needs Pinch Of Artificial Intelligence

DevOps helped technologists save time such drastically that the projects that were barely deployed in a year or more are now seeing the daylight in just months or even weeks. It removed communication bottlenecks, eased the change management, and helped with an end-to-end automation cycle for the SDLC. However, as has been the interesting feature of humanity, any innovation that eases our life also brings with it challenges of its own. Bending over backward, the business leaders now have much more complex customer demands and employee skillset requirements to live up to. Digital Modernization requires rapid and complex processes that move along the CI/CD pipeline with all sorts of innovative QA automation, Complex APIs, Configuration Management Platforms, and Infrastructure-as-a-Code, among other dynamic technology integrations. Such complexities are making DevOps turn on its head due to a serious lack of visibility over the workloads. It is, therefore, time for the companies to put their focus to an essential part of their digital transformation journey – the Monitoring. Continuous Monitoring for the DevOps of Our Times DevOps monitoring is a proactive approach that helps us detect the defects in the CI/CD pipeline and strategize to resolve them. Moreover, a good monitoring strategy can curb potential failures even before they occur. In other words, one cannot hold the essence of DevOps frameworks with their time-to-market benefits without having a good monitoring plan. With the IT landscape getting more and more unpredictable with each day, even DevOps monitoring solutions need to evolve into something more dynamic than its traditional ways. Therefore, it is time for global enterprises and ISVs to adopt Continuous Monitoring. Ideally, Continuous Monitoring or Continuous Control Monitoring in DevOps refers to end-to-end monitoring of each phase in the DevOps pipeline. It helps DevOps teams gain insight into the CI/CD processes for their performance, compliance, security, infrastructure, among others, by offering useful metrics and frameworks. The different DevOps phases can be protected with easy threat assessments, quick incident responses, thorough root cause analysis, and continuous general feedback. In this way, Continuous Monitoring covers all three pillars of a contemporary software – Infrastructure, Application, and Network. It is capable of reducing system downtimes by rapid responses, full network transparency and proactive risk management. There’s one more technology that the technocrats handling the DevOps of our times are keen to work on – Artificial Intelligence (AI). So it wouldn’t be a surprise if the conversations about Continuous Monitoring being fuelled by AI are already brewing up. However, such dream castles need a concrete technology-rich floor. Therefore, we will now look at the possibilities for implementing Continuous DevOps Monitoring Solutions with Artificial Intelligence holding the reins. Artificial Intelligence for Continuous Monitoring As discussed above Continuous Monitoring essentially promises the health and performance efficiency of the infrastructure, application, and network. There are solutions like Azure DevOps Monitoring, AWS DevOps monitoring and more that offer surface visibility dashboards, custom monitoring metrics, hybrid cloud monitoring, among other benefits. So, how do we weave in Artificial Intelligence into such tools and technologies? It mainly comes down to collecting, analyzing, and processing the monitoring data coming in from the various customized metrics. In fact, a more liberal thought can be given even to accommodate setting up these metrics throughout the different phases of DevOps. So, here’s how Artificial Intelligence can help with Continuous Monitoring and empower the DevOps teams to navigate the complex nature of modern applications. Proactive Monitoring AI can enable the DevOps pipeline to quickly analyze the data coming in from monitoring tools and raise real-time notifications for any potential downtime issues or performance deviations. Such analysis might exhaust much more manual workforce than AI-based tools that can automatically identify and update about unhealthy system operations much more frequently and efficiently. Based on the data analysis, they can also help customize the metrics to look for more vulnerable performance points in the CI/CD pipeline for a more proactive response. Resource-Oriented Monitoring One of the biggest challenges while implementing Continuous Monitoring is the variety of infrastructure and networking resources used for the application. The uptime checks, on-premise Monitoring, component health checks are different in Hybrid cloud and Multi-cloud environments. Therefore, monitoring such IT stacks and for an end-to-end DevOps might be a bigger hassle than one can imagine. However, AI-based tools can be programmed to find unusual patterns even in such complex landscapes by tracking various system baselines. Furthermore, AI can also quickly pin-point the specific defective cog in the wheel that might be holding the machinery down. Technology Intelligence The built-in automation and proactiveness of Artificial Intelligence enables it to relax the workforce and the system admins by identifying and troubleshooting the complicated systems. Whether it is a Kubernetes cluster, or a malfunctioning API, AI can support the monitoring administrators to have an overall visibility and make informed decisions about the DevOps apparatus. Such technology intelligence would otherwise require a very unique skillset that might be too easy to hire or acquire. Therefore, enterprises and ISVs can turn to AI for empowering their DevOps monitoring solutions and teams with the required support. Conclusion DevOps is entering the phase of specializations. AIOps, DevSecOps, InfraOps and more are emerging to help the industries with their specific and customized DevOps automation needs. Therefore, it is necessary that the DevOps teams have the essential monitoring resources to ensure minimal to no failures. Continuous Monitoring aided by Artificial Intelligence can provide the robust mechanism that would help the technology experts mitigate the challenges of navigating the complex digital landscape thus, helping the global industries with their digital transformation ambitions.

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7 Ways AI Speeds Up Software Development in DevOps

I am sure we all know that the need for speed in the world of IT is rising every day. The software development process that used to take much longer in the early stages is now being executed in weeks by collaborating distributed teams using DevOps methodologies. However, checking and managing DevOps environments involves an extreme level of complexity. The importance of data in todays’ deployed and dynamic app environments has made it tough for DevOps teams to absorb and execute data efficiently for identifying and fixing client issues. This is exactly where Artificial Intelligence and Machine Learning comes into the picture to rescue DevOps. AI plays a crucial role in increasing the efficiency of DevOps, where it can improve functionality by enabling fast building and operation cycles and offering an impeccable client experience on these features. Also, by using AI, DevOps teams can now examine, code, launch, and check software more efficiently. Furthermore, Artificial Intelligence can boost automation, address and fix issues quickly, and boost cooperation between teams. Here are a few ways AI can take DevOps to the next level. 1. Added efficiency of Software Testing The main point where DevOps benefits from AI is that it enhances the software development process and streamlines testing. Functional testing, regression testing, and user acceptance testing create a vast amount of data. And AI-driven test automation tools help identify poor coding practices responsible for frequent errors by reading the pattern in the data acquired by delivering the output. So, this type of data can be utilized to improve productivity. 2. Real-time Alerts Having a well-built alert system allows DevOps teams to address defects immediately. Prompt alerts enable speedy responses. However, at times, multiple alerts with the same severity level make it difficult for tech teams to react. AI and ML help a DevOps team to prioritize responses depending on the past behavior, the source of the alerts, and the depth. And can also recommend a prospective solution and help resolve the issue quicker. 3. Better Security Today, DDoS (Distributed Denial of Service) is very popular and continuously targets organizations and small and big websites. AI and ML can be used to address and deal with these threats. An algorithm can be utilized for differentiating normal and abnormal conditions and take actions accordingly. Developers can now make use of AI to improve DevSecOps and boost security. It consists of a centrally logging architecture for addressing threats and anomalies. 4. Enhanced Traceability AI enables DevOps teams to interact more efficiently with each other, particularly across long distances. AI-driven insights can help understand how specifications and shared criteria represent unique client requirements, localization, and performance benchmarks. 5. Failure Prediction Failure in a particular tool or any in area of DevOps can slow down the process and reduce the speed of the cycles. AI can read through the patterns and anticipate the symptoms of a failure, especially when a pre-happened issue creates definite readings. At the same time, the ML models can help predict an error depending on the data. AI can also see signs that we humans can’t notice. Therefore, these early notifications help the teams address and resolve the issues before impacting the SDLC (Software Development Life Cycle). 6. Even Faster Root Cause Analysis To find the actual cause of a failure, AI makes use of the patterns between the cause and activity to discover the root cause behind the particular failure. Engineers are often too preoccupied with the urgency to going Live and don’t investigate the failures thoroughly. Though they study and resolve issues superficially, they mostly avoid detailed root cause analysis. In such cases, the root cause of the issue remains unknown. Therefore, it is essential to conduct the root cause analysis to fix a problem permanently. And AI plays a crucial role here in these types of cases. 7. Efficient Requirements Management DevOps teams make use of AI and ML tools to streamline each phase of requirements management. Phases such as creating, editing, testing, and managing requirements documents can be streamlined with the help of AI. The AI-based tools identify the issues covering unfinished requirements to escape clauses, enhancing the quality and the accuracy of requirements. Wrapping Up Today, AI speeds up all phases of DevOps software development cycles by anticipating what developers need before even requesting for it. AI improves software quality by giving value to specific areas in DevOps, such as improved software quality with automated testing, automatically recommending code sections, and organizing requirement handling. However, AI must be implemented in a controlled manner to make sure that they become the backbone of the DevOps system and does not act as rogue elements that require continuous remediation.

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5 Tips To Build A Fail-Proof DevSecOps Culture

A simple yet overlooked concept lies at the heart of a successful DevOps initiative: Developers drive the software agenda, so developer participation is essential for achieving a more secure framework. That is where the term DevSecOps comes into play – and more importantly, the practices and culture it represents – can begin to make a huge difference. A solid DevSecOps culture suits our evolving hybrid computing environments, faster and more frequent software delivery, and other demands for modern IT. This is the main reason why DevSecOps matters to IT leaders. DevSecOps helps ship safer applications by prioritizing secure development alongside speed by making security part of the current DevOps pipeline. It’s more than just reviewing the security vulnerabilities or sorting through false positives. Here are 5 essential tips for nurturing a DevSecOps culture of your own – and using the metrics to gauge success. 1. No “one size fits all” concept A downside of a methodological and cultural shift like DevSecOps is that people might assume there’s just a single “right” way of doing DevSecOps. But that’s not true. Not all enterprises are built equal, which is why there’s more than just one model to implement DevSecOps. You can take your security staff and embed them into your DevOps teams. Or you can train up your developers to become the embedded security experts. Or you can build cross-functional teams or task forces. It’s simply any combination that works organizationally and culturally. These setups share a standard denominator core to DevSecOps: Recognizing and addressing security concerns as early as possible. So that any of them can help endorse a powerful DevSecOps culture, given they make better sense for your organization and culture. 2. Transparency If you think the battle between traditional development processes and operations silos was bad, well, those teams were comparatively agile compared to the traditional isolation of security teams. Strangely, most of these silos are deliberately created by the workforce because they believe it makes them more secure. But it doesn’t. All these silos create an incapacity for each team to speak the same language. As a result, they face difficulty in translating what they do back into people and processes. Getting rid of the isolation of security teams and making use of some model that better combines multiple roles and responsibilities together and can yield meaningful benefits. The foundation of a thriving DevSecOps culture is total organizational transparency, including all the aspects of the IT department, which implies that security can no longer be siloed. Enterprises going through a digital transformation or developing modern applications work off the same data through various lenses, bringing together everyone instead of creating silos. 3. Security education and training investment for Developers Training and educating software developers (and related job titles and roles) is an excellent step toward a healthy DevSecOps culture. It’s because security is everyone’s responsibility, and it’s essential to arm everyone with the right knowledge and tools required to make that so. The developers who previously didn’t have to bear much responsibility for the security of their code can’t be suddenly expected to bring in the hardcore security know-how of a white-hat hacker. But if you do invest in enhancing your developers’ security knowledge and tools, everyone benefits from it. Today’s IT leaders must invest in security training, which can come in the form of short sprints, code review, understanding which libraries are safe to use, or setting up feature flags that will review the code accurately, one piece at a time. This way, if anything goes wrong, the DevSecOps team can immediately get into the quality assurance mindset for applying fixes accordingly, with security as a top priority. 4. Make “sec” in security silent The key to a perfect DevSecOps culture is to eliminate as much friction as possible from processes. The perfect way to think about implementing security into DevSecOps is to make ‘Sec’ silent. To lessen friction or make security “silent,” include automation into your security processes and tools. The ultimate purpose is to enable DevOps teams to implement security automatically as part of their everyday processes. By implementing security controls directly into the CI/CD pipeline and taking development tools as an example, you’ve got good options at your disposal, including plenty of open source platforms. From a technical perspective, an excellent place to start is to make sure each team makes use of the available open source tools to perform security-related tasks. Configuration management tools also have made the integration of operations and security a much easier proposition. 5. Shared goals and KPIs A robust DevSecOps culture also depends on eliminating the conflicting performance incentives across various roles on the same team. A typical struggle in this category would be for developers who are measured almost solely by how quickly and frequently they ship code and security pros tasked with limiting vulnerabilities in production. One wants to move as fast as possible; the other is motivated to slow down everything. DevSecOps must be, in part, about getting people on the same page, working toward collective goals – with shared responsibilities and metrics. There are numerous key performance indicators as examples for measuring the DevSecOps efforts. Everyone should share in the responsibility for these measurements and not just the security team: Number of app security issues discovered in production: You want this number to decrease. Issues identified in production are issues missed during the development period, so this number should be minimized. Percentage of deployments stopped/delayed due to failing security tests: Ideally, such issues should be resolved before deployment. Time to fix security issues: This is a time-consuming approach that must decrease over time; it should be a reward for a healthy DevSecOps culture. In that, it reduces the effort and pain involved in resolving security issues when they do occur. Hopefully, issues that are discovered pre-integration are easier and faster to fix, so this is also a perfect picture of how well the team is performing. Takeaway Enterprises that values security see it to be a culture rather than just a step. And for this to be accomplished, it’s crucial to have a robust DevSecOps culture. With this, security won’t be viewed just as a technological flaw and won’t be ignored. It’ll be prioritized, and the ways discussed above are a few of the ideas on how your organization can go ahead and implement this.

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Kubernetes storage validation by Ansible test automation framework

Ansible is mainly used for software provisioning, configuration management, and application-deployment tool. We have used it for developing test automation framework to validate Kubernetes storage. How we used ansible as a test automation tool to validate Kubernetes storage is explained in this post.Why we used ansible?Kubernetes is a clustered environment where we will have 2 or more worker nodes and one or more master node. We have to create CSI driver volumes in it to validate our storage box.So, the test environment will consist of multiple hosts. The volume may be mount on any of the pod created in any of the worker nodes. So dynamically, we need to validate any of the worker nodes. If we use any programming/scripting languages, then we need to handle remote code execution. We worked in a couple of automation projects using PowerShell and python. But remote code execution library needs a lot of work. But in ansible, the heavy lifting of remote execution is taken care of by itself. So, we can only concentrate only on core test validation logicHow ansible is used?As part of the Kubernetes storage validation, there are many features to be validated.Features such as Volume group, Snapshot group, Volume mutator, Volume resize need to be validated. Each feature will have many test cases.For each feature, we created a role. Each test is covered in tasks file under role.In main.yml in roles will call all the test tasks file.Structure of ansible automation framework rolesroles Feature_test volumegroup_provision Tasks Test1.yml Test2.yml Main.yml volumesnapshot_provision volume_resize basic_volume_workflow Lib resources (library files sc,pvc,pod and IO inside Pod) volgroup_play.yml volsnaphost_play.yml volresize_play.yml basic_volume_play.yml Hosts In the above framework, test1.yml and test2.yml are tasks file where test cases would be written. Each feature will have its own play file—for example, Volgroup_play.yml. So if we execute volgroup_play.yml, then tests reside in test1.yml and test2.yml will be executed. Below command will execute the play ansible-playbook -I hosts volgroup_play.yml -vvChallenges:Problem:In ansible, if a task is failed, then execution will be stopped. So, if 10 test cases are there in a feature, and if a second test is failed, then remaining 8 test cases will not be executed.Solution:Each test case is written inside block and rescue. So, when testing is failed, it will be handled by a rescue block. In the rescue block, we will clean up the testbed so that the next test case will be executed without any issues.Sample test file.- Block: - include: test_header vars: Test_file: ‘test1.yml’ Test_description: ‘volume group provision basic workflow’ < creation of SC,PVC and POD and validation logic> - include: test_footer vars: Test_file: “test1.yml” Test_description: ‘volume group provision basic workflow’ Test_result: “Pass” rescue: - include: test_footer vars: Test_file: “test1.yml” Test_description: ‘volume group provision basic workflow’ Test_result: “Fail” < Cleanup logic>Problem:Some of the tasks which can be done easier in a programming language are tough in ansible.Solution:Write custom ansible module using python.Pros of using ansible as automation framework:Ansible is very simple to implement.It takes care of heavy lifting of remote code executionFor clustered environment, speed of automation development is considerably higher.Cons:Though it is simple, still ansible is not programming language. When straightforward commands are written, it will be easier. but when we write logic, few lines of programming language will do what 100 lines of ansible does.When multiple tasks need to be executed in nested loop passion, it will be very hard to implement that in ansible. (we have to use ‘include’ module with loops then again use ‘include’ modules. It is not very intuitive)Conclusion:Ansible can be used as a test automation framework for Kubernetes storage validation. Wherever heavy programming logic is required , it is better to use custom ansible module using python which will make life easier..filledCheckboxes input[type="checkbox"]{opacity:0;display :none;}.multiStepFormBody button.close{z-index:99;}

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5 key ingredients of Microservices Architecture (MSA) you should not ignore

At the helm of Information Technology is the innovation of cutting-edge practices that optimize the complete software delivery lifecycle. One such outcome of this innovative mindset is Microservices Architecture (MSA). Microservices comes from the family of Cloud-Native that aims to change the implementation of backend services radically. In no time, Microservices has emerged as a digital disruptor and a differentiator to stay ahead of the competition. Per statistics, Microservices reduced the overall development time by a whopping 75 percent. What drive-through did to the food industry, Microservices are doing to Software Industry The invention of a drive-through in America revolutionized the culture of fast food. People were served food on the go, real-fast and hot. The idea was such a hit that other businesses jumped on the bandwagon. Drive-through established itself as the ultimate fast-track platform for delivering products/services efficiently. Just like a drive-through, Microservices are enabling the pinnacle of efficiency in software development. The main aim of Microservices is to shy away from the monolithic application delivery. It breaks down your application components into standalone services (Microservices). These services then must undergo development, testing, and deployment in different environments. The services’ numbers can be in 100s or 1000s. Additionally, teams can use various tools for each service. The resultant will be mammoth tasks coupled with an exponential burden on the operations. The process complexities and time-battle will also be a nightmare. Companies such as Netflix and Amazon have lauded the benefits of Microservices. It instills application scalability and drives product release speed. Companies also leverage Microservices to stay nimble and boost their product features. Microservices function effortlessly when a few key ingredients from a part of its architecture. Let’s study them. 1. Continuous Integration and Continuous Deployment (CI/CD) From a release standpoint, Microservices needs to ensure a continuous loop of software development, testing, and release. Therefore, when you look at Microservices and its practical implementation, you cannot ignore CICD. Establishing a CICD pipeline through Infrastructure as Code (IaC) minimal operational hurdles and deliver a better user experience in the application management. 2. API Gateway for request handling Microservices leverage different communication protocols for internal use. The API Gateway will route HTTP requests via reverse proxy towards endpoints of internal Microservices. The API gateway works as the single URL source for application to map their request internally to the Microservices. An API’s key functions are Authentication, Authorization, Logging, and Proxying. With an API gateway, it becomes easy to invoke these functionalities at desired efficiency. API gateway also helps Microservices to retrieve data from multiple services in one-go, thereby improving overhead and overall user experience. 3. Toolchain for automation CICD and Microservices work hand in glove. Your Microservices architecture needs a set of the toolchain that powers automation to ensure the CICD pipeline is well oiled for uninterrupted performance. These tools span build environment, testing, and regression, deployment, image registry, and platform. 4. Configuration component to save time The idea is to avoid restructuring while running multiple configurations in Microservices. There are multiple configurations used in different services. These include formats, date, time, etc. With rising service requests managing these configurations becomes treacherous. Further, these configurations mustn’t be held static, rather they should run dynamically to suit multiple environments. Also, storing such configuration in source code will affect the API. Therefore, it is essential to use a component for managing configuration. 5. Infrastructure Scalability and Monitoring Microservices involves multiple deployments of APIs across the IT infrastructure. This means it is essential that infrastructure provisioning is in the auto-pilot mode to ensure APIs run independently. Therefore, it is viable to have a robust infrastructure that can scale on demand while maintaining performance and efficiency. Infrastructure monitoring is a key aspect of Microservices, which is also a distributed architecture. Distributed tracing becomes critical to ensure efficient tracking of multiple services at different endpoints allowing complete visibility. What do we infer Microservices is slated for widespread adoption without a doubt. As cloud-native technologies gain traction, Microservices would increasingly become a necessity. By 2025, we should expect 90 percent of the applications depending on Microservices architecture. Before any organization thinks of reaping the benefit of Microservices for scalability, they must remember one thumb rule – the real potential is hidden in its building blocks discussed above. These blocks ensure that one gets a robust Microservices architecture to enable continuous software delivery and upgrade practices.

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Kubernetes – Bridging the Gap between 5G and Intelligent Edge Computing

PrologueIn the era of digital transformation, the 5G network is a leap forward. But frankly, the tall promises of the 5G network are cornering the edge computing technology to democratize data at a granular level. To add to the vows, 5G also demands that edge computing enhances performance and latency while slashing the cost. Kubernetes – an open-source container-orchestration is a dealmaker between 5G and edge computing.In this blog, you will read:A decade defined by the cloudThe legend of cloud-native ContainersThe rise of Container Network Functions (CNFs)Edge computing must reinvent the wheelKubernetes – powering 5G at the edgeKubeEdge – giving an edge to KubernetesA decade defined by the cloudWhat oil is to the automobile industry, the cloud is to Information Technology (IT) industry. Cloud revolutionized the tech space by making data available at your fingertips. Amazon’s Elastic Compute Cloud (EC2) planted the seed of the cloud somewhere in the early 2000s. Google Cloud and Microsoft Azure followed this. However, the real growth of cloud technology skyrocketed only after 2010-2012.Numbers underlining the future trends– Per Cisco, cloud computing will process more than 90 percent of the workloads in 2021– PerRightScale, the business run around 41 percent workloads in private cloud and 38 percent in the public cloud– Per Cisco, 75 percent of all compute instance and cloud workloads will be SaaS by the end of 2021The legend of cloud-native ContainersThe advent of cloud-native is a hallmark of evolutionary development in the cloud ecosystem. The fundamental nature of the architecture of cloud-native is the abstraction of multiple layers of the infrastructure. This means a cloud-native architect has to define those layers via code. And when coding, one gets a chance to include top functionalities to maximize the value of the business. Cloud-native also empowers coders to create scripts for infrastructure scalability.Cloud-native container tech is making a noteworthy contribution to the future growth of the cloud-native ecosystem. It is playing a more significant role in enabling capabilities of the 5G architecture in real-time. With container-focused web services, 5G network companies can achieve resource isolation and reproducibility to drive resiliency and faster deployment. Containers make the process of deployment less intricate, which powers the 5G infrastructure to scale data requirements spanning cloud networks. Organizations can leverage Containers to process data and compute it on a massive scale.A conflation of Containers and DevOps work magic for 5G. Bringing these loosely coupled services will help 5G providers to automate application deployment, receive feedback swiftly, eliminate bottlenecks, and achieve a self-paced continuous improvement mechanism. They can provision resources on-demand with unified management across a hybrid cloud.The fire of cloud-native is ignited in the telecom sector. The coming decade – 2021-2030, will witness it spread like wildfire.The rise of the Container Network Functions (CNFs)We witnessed the rise of Container Network Functions (CNFs), while network providers were using containers with VMware and virtual network functions (VNF). CNFs are functions of a network that can run on Kubernetes across multi-cloud and/or hybrid cloud infrastructure. CNFs are ultra-lightweight compared to VNFs, which traditionally operate in the VMware environment. This makes CNFs super portable and scalable. But, the underlining factor in the CNF architecture is that it is deployable over a bare metal server that brings down the cost dramatically.5G – the next wave in the telecom sector promises to offer next-gen services entailing automation, elasticity, and transparency. Looking at the requirement micro-segmented architectures, VNF (VMware environment) would not be an ideal choice for 5G providers. Logically, the adoption of CNFs is a natural step forward. Of course, doing away entirely with VMware isn’t anytime on the board. Therefore, a hybrid model of VNF and CNF sounds good.Recently, Intel, in collaboration with Red Hat, created a cloud-based onboarding service and test bed to conflate CNF (containerized environment) and VNF (VMware environment). The test bed is expected to enhance compatibility between CNF and VNF and slash the deployment time. The architecture looks like the image below.Edge computing must reinvent the wheelMultiple devices generate a massive amount of data concurrently. To enable cloud centers to process such data is a herculean task. Edge computing architecture puts infrastructure close to data devices within a distributed environment that results in faster response time and lower latency. Edge computing’s local processing of data simplifies the process and reduces the overall costs. Edge computing has been working as a catalyst for the telecommunication industry to date. However, with 5G in the picture, the boundaries are all set to push.The rising popularity of the 5G network is putting a thrust on intuitive experiences in real-time. 5G catapults the speed of the broadband by up to 10x and plummets the device density by around a million devices/sq.km. For this, 5G requires ultra-low latency, which can be created by a digital infrastructure powered by edge computing.Honestly, edge computing must start flapping its wings for the success of the 5G network. It must ensure– Better device management– Lesser resource utilization– More lightweight capabilities– Ultra-low latency– Increased security blanket and data transfer reliabilityKubernetes – powering 5G at the edge“Kubernetes (K8s) is an open-source system for automating deployment, scaling, and management of containerized applications. It groups containers that make up an application into logical units for easy management and discovery.” Kubernetes.ioKubernetes streamlines the underlying compute spanning distributed environment and imparts consistency at the edge. Kubernetes helps network providers maximize the value of Containers at the edge by automation and swift deployment with a broader security blanket. Kubernetes for edge computing will eliminate most of the labor-intensive workloads, thereby, driving better productivity and quality.Kubernetes has an unquestionable role to play in unleashing the commercial value of 5G, at least for now. The only alternative to Kubernetes is VMware, which does not make the cut due to space and cost issues. Kubernetes architecture has proved to accelerate the automation of mission-critical workloads and reduce the overall cost of 5G deployment.A Microservices architecture is required to support non-real-time components of 5G. Kubernetes can create a self-controlled closed loop, which ensures a required number of Microservices are hosted and controlled at the desired level. Further, the Horizontal Pod Autoscaler of Kubernetes can release new container instances depending on the workload at the edge.Last year, AT&T signed an eight-figure and multi-year deal with Mirantis to roll out 5G leveraging OpenStack and Kubernetes. Ryan Van Wyk, AT&T Associate VP of the Network, had quoted, “There really isn’t much of an alternative. Your alternative is VMware. We’ve done the assessments, and VMware doesn’t check boxes we need.”KubeEdge – giving an edge to KubernetesKubeEdge is an open-source project built on Kubernetes. The latest version, KubeEdge v1.3, hones the capabilities of Kubernetes to power intelligent orchestration of containerized application at the edge. KubeEdge streamlines communication between edge and cloud data center by infrastructure support for network, app. deployment, and metadata. The best part is that it allows coders to create a customized logic script to enable resource-constrained device communication at the edge.Future aheadGartner quotes, “Around 10 percent of enterprise-generated data is created and processed outside a traditional centralized data center or cloud. By 2025, this figure will reach 75 percent.”The proliferation of devices due to IoT, Big Data, and AI will generate data of mammoth amount. For the success of 5G, it is essential that edge computing handles these complex workloads and maintains data elasticity. Therefore, Kubernetes will be the functional backbone of edge computing imparting resiliency in orchestrating containerized applications.

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Making DevOps Sensible with Assembly Lines

DevOps heralded an era of cutting edge practices in software development and delivery via Continuous Integration (CI) Pipelines. CI made DevOps an epitome of software development and automation, entailing the finest agile methodologies. But, the need for quicker development, testing, and deployment is a never-ending process. This need is pushing back the CI and creating a space for a sharper automation practice, which runs beyond the usual bits and pieces automation. This concept is known as DevOps Assembly Lines.Borrowing inspiration from Automobile IndustryThe concept of assembly lines was first started at Ford Plant in the early 20th century – the idea improved continuously and today is powered via automation. Initially, the parts of the automobiles were manufactured and assembled manually. This was followed by automation in manufacturing, while the assembly was manual. So, there were gaps to be addressed for efficiency, workflow optimization, and speed. The gaps were addressed by automating the assembly of parts. Something similar is happening in the SDLC via DevOps Assembly Lines.Organizations that implement advanced practices of DevOps follow a standardized and methodological process throughout the teams. As a result, these organization experiences fast-flowing CI pipelines, rapid delivery, and top quality.A silo approach that blurs transparencyFollowing the DevOps scheme empowers employees to deliver their tasks efficiently and contribute to the desired output of their team. Many such teams within a software development process are leveraging automation principles. The only concern is that this teamwork is in silos hindering overall visibility into other teams’ productivity, performance, and quality. Therefore, the end product falls shorts of desired expectations – often leaving teams perplexed and demotivated. This difference in DevOps maturity within different teams in a software development environment calls for a uniform Assembly Line.Assembly Lines – triggering de-silo of fragmented teamsCI pipelines consist of a host of automated activities that are relevant to individual stages in the software lifecycle. Which means there are a number of CI pipelines operating simultaneously; but, it is fragmented within SDLC. Assembly Lines is an automated conflation of such CI pipelines towards accelerating a software product’s development and deployment time. DevOps Assembly Line automates activities like continuous integration in the production environment, configuration management and server patching for infrastructure managers, reusable automation scripts in the testing environment, and code as monitoring scripts for security purposes.Bridging the gap between workflows, tools and platformsDevOps Assembly Lines creates a perfect bridge, finely binding standalone workflows, and automated tools and platforms. This way, it establishes a smoothly integrated chain of deployment pipeline optimized for the efficient delivery of software products. The good part is it creates an island of connected and automated tools and platforms; these platforms belong to different vendors and are that gel together easily. Assembly Lines eliminates the gap between manual and automated tasks. It brings QAs, developers, operations teams, SecOps, release management teams, etc. on a single plane to enable a streamlined and uni-directional strategy for product delivery.Managed platform as a service approach for managementDevOps Assembly Lines exhibits an interconnected web of multiple CI pipelines, which entail numerous automated workflows. This makes the management of Assembly Lines a bit tricky. Therefore, Organizations can leverage a managed services portal that streamlines all the activities across the DevOps Assembly Lines.Installing a DevOps platform will centralize the activities of Assembly Lines and streamline a host of workflows. It will offer a unified experience to multiple DevOps teams and also help operate a low cost and fast-paced Assembly Lines. A DevOps platform would also entail different tools from multiple vendors that could work in tandem.The whole idea behind installing Assembly Lines is to establish a collaborative auto-mode within diverse activities of SDLC. A centralized, on-demand platform could help get started with pre-integrated tools, that could manage automated deployment.A team of operators, either in-house or via a support partner, could handle this platform. This way, there will be smooth functioning across groups, and on-demand requests for any issues that could be addressed immediately. The platform will invariably help DevOps architects to concentrate on productive parts – while maintenance is taken care of behind the scenes. Further, it would allow teams to look beyond their core activities (a key goal of Assembly Lines) and absorb the status of overall team productivity. The transparency will give them an idea of existing hindrances, performances, productivity, and expected quality. In accordance, they could take corrective measures.Future AheadCI pipelines are helpful for rapid product development and deployment. But, considering the graph of rising expectation in quality and feature enablement and considering the time-to-market requirement, the CI pipelines do not fit the bill. Further, the issue of configuration management is too complicated for CI pipelines to handle. Therefore, the next logical step is to embrace DevOps Assembly Lines. And the importance of a centralized management platform to drive consistency, scalability, and transparency via Assembly Lines should not be undermined.

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Site Reliability Engineering (SRE) 101 with DevOps vs SRE

Consider the scenario belowAn Independent Software Provider (ISV) developed a financial application for a global investment firm that serves global conglomerates, leading central banks, asset managers, broking firms, and governmental bodies. The development strategy for the application encompassed a thought through DevOps plan with cutting-edge agile tools. This has ensured zero downtime deployment at maximum productivity. The app now handles financial transactions in real-time at an enormous scale, while safeguarding sensitive customer data and facilitating uninterrupted workflow. One unfortunate day, the application crashed, and this investment firm suffered a severe backlash (monetarily and morally) from its customers.Here is the backstory – application’s workflow exchange had crossed its transactional threshold limit, and lack of responsive remedial action crippled the infrastructure. The intelligent automation brought forth by DevOps was confined mainly to the development and deployment environment. The IT operations, thus, remained susceptible to challenges.Decoupling DevOps and RunOps – The Genesis of Site Reliability Engineering (SRE)A decade or two ago, companies operated with a legacy IT mindset. IT operations consisted mostly of administrative jobs without automation. This was the time when the code writing, application testing, and deploying was done manually. Somewhere around 2008-2010, automation started getting prominence. Now Dev and Ops worked concurrently towards continuous integration and continuous deployment – backed by the agile software movement. The production team was mainly in charge of the runtime environment. However, they lacked skillsets to manage IT operations, which resulted in application instability, as depicted in the scenario above.Thus, DevOps and RunOps were decoupled, paving the way for SRE – a preventive technique to infuse stability in the IT operations.“Site Reliability Engineering is the practice and a cultural shift towards creating a robust IT operation process that would instill stability, high performance, and scalability to the production environment.”Software-First Approach: Brain Stem of SRE“SRE is what happens when you ask a software engineer to design an operations team,” Benjamin Treynor Sloss, Google. This means an SRE function is run by IT operational specialists who code. These specialist engineers implement a software-first approach to automate IT operations and preempt failures. They apply cutting edge software practices to integrated Dev and Ops on a single platform, and execute test codes across the continuous environment. Therefore, they carry advanced software skills, including DNS Configuration, remediating server, network, and infrastructure problems, and fixing application glitches.The software approach codifies every aspect of IT operations to build resiliency within infrastructure and applications. Thus, changes are managed via version control tools and checked for issues leveraging test frameworks, while following the principle of observability.The Principle of Error BudgetSRE engineers verify the code quality of changes in the application by asking the development team to produce evidence via automated test results. SRE managers can fix Service Level Objectives (SLOs) to gauge the performance of changes in the application. They should set a threshold for permissible minimum application downtime, also known as Error Budget. If a downtime during any changes in the application is within the scale of the Error Budget, then SRE teams can approve it. If not, then the changes should be rolled back for improvements to fall within the Error Budget formula.Error Budgets tend to bring balance between SRE and application development by mitigating risks. An Error Budget is unaffected until the system availability will fall within the SLO. The Error Budget can always be adjusted by managing the SLOs or enhancing the IT operability. The ultimate goal remains application reliability and scalability.Calculating Error BudgetA simple formula to calculate Error Budget is (System Availability Percentage) minus (SLO Benchmark Percentage). Please refer to the System Availability Calculator below.Illustration.Suppose the system availability is 95%. And, your SLO threshold is 80%.Error Budget: 95%-80%= 15%AvailabilitySLA/SLO TargetError BudgetError Budget per Month (30 days)Error Budget per Quarter (90 days)95%80%15%108 hours324 hoursError Budget/month: 108 hours. (At 5% downtime, per day downtime hours is 1.2 hours. Therefore for 15% it is 1.2*3 = 3.6. So for 30 days it will be 30*3.6 = 108 hours)Error Budget/quarter: 108*3 = 324 hours.Quick Trivia – Breaking monolithic applications lets us derive SLOs at a granular level.Cultural Shift: A Right Step towards Reliability and ScalabilityPopular SRE engagement models such as Kitchen Sink, a.k.a. “Everything SRE” – a dedicated SRE team, Infrastructure – a backend managed services or Embedded – tagging SRE engineer with developer/s, require additional hiring. These models tend to build dedicated teams that lead to a ‘Silo’ SRE environment. The problem with the Silo environment is that it promotes a hands-off approach, which results in a lack of standardization and co-ordination between teams. So, a sensible approach is shelving off a project-oriented mindset and allowing SRE to grow organically within the whole organization. It starts by apprising the teams of customer principles and instilling a data-driven method for ensuring application reliability and scalability.Organizations must identify a change agent who would create and promote a culture of maximum system availability. He / She can champion this change by practicing the principle of observability, where monitoring is a subset. Observability essentially requires engineering teams to be vigilant of common and complex problems hindering the attendance of reliability and scalability in the application. See the principles of observability below.The principle of observability follows a cyclical approach, which ensures maximum application uptime.Step Zero – Unlocking Potential of Pyramid of ObservabilityStep zero is making employees aware of end-to-end product detail – technical and functional. Until an operational specialist knows what to observe, the subsequent steps in the pyramid of observability remain futile.Also, remember that this culture shift isn’t achievable overnight – it will be successful when practiced sincerely over a few months.DevOps vs. SREPeople often confuse SRE with DevOps. DevOps and SRE are complementary practices to drive quality in the software development process and maintain application stability.Let’s analyze four key the fundamental difference between DevOps and SRE.ParameterDevOpsSREMonitoring vs. RemediationDevOps typically deals with the pre-failure situation. It ensures conditions that do not lead to system downtime.SRE deals with the post-failure situation. It needs to have a postmortem for root cause analysis. The main aim is to ensure maximum uptime and weed out failures for long term reliability.Role in Software Development Life Cycle (SDCL)DevOps is primarily concerned with the efficient development and effective delivery of software products. It must ensure Zero Down Time Deployment (ZDD). It also requires to identify blind spots within infrastructure and application.SRE is managing IT operations efficiently once the application is deployed. It must ensure maximum application uptime and stability within the production environment.Speed and Cost of Incremental ChangeDevOps is all about rolling out new updates/features, faster release cycle, quicker deployment and continuous integration, and continuous development. The cost of achieving all this isn’t of significance.SRE is all about instilling resilience and robustness in the new updates/features. However, it expects small changes at frequent intervals. This gives a larger room to measure those changes and adopt corrective measures in case of a possible failure. The bottom line is efficient testing and remediation to bring down the cost of failure.Key MeasurementsDevOps’ measurement plan revolves around CI/CD. It tends to measure process improvements and workflow productivity to maintain a quality feedback loop.SRE regulates IT operations with some specific parameters like Service Level Indicators (SLIs) and Service Level Objectives (SLOs).Conclusion – SRE Teams as Value CenterA software product is expected to deliver uninterrupted services. The ideal and optimal condition is maximum uptime with 24/7 service availability. This requires unmatched reliability and ultra-scalability.Therefore, the right mindset will be to treat SRE teams as a value center, which carries a combination of customer-facing skills and sharp technical acumen. Lastly, for SRE to be successful, it is necessary to create SLI driven SLOs, augment capabilities around cloud infrastructure, a smooth inter-team co-ordination, and thrust Automation and AI within IT operations.

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devops Site Reliability Engineering SRE

Decoding the Self-Healing Kubernetes: Step by Step

PrologueBusiness application that fails to operate 24/7 would be considered inefficient in the market. The idea is that applications run uninterrupted irrespective of a technical glitch, feature update, or a natural disaster. In today’s heterogeneous environment where infrastructure is intricately layered, a continuous workflow of application is possible via self-healing.Kubernetes, which is a container orchestration tool, facilitates the smooth working of the application by abstracting machines physically. Moreover, the pods and containers in Kubernetes can self-heal.Captain America asked Bruce Banner in Avengers to get angry to transform into ‘The Hulk’. Bruce replied, “That’s my secret Captain. I’m always angry.”You must have understood the analogy here. Let’s simplify – Kubernetes will self-heal organically, whenever the system is affected.Kubernetes’s self-healing property ensures that the clusters always function at the optimal state. Kubernetes can self-detect two types of object – podstatus and containerstatus. Kubernetes’s orchestration capabilities can monitor and replace unhealthy container as per the desired configuration. Likewise, Kubernetes can fix pods, which are the smallest units encompassing single or multiple containers.The three container states include1. Waiting – created but not running. A container, which is in a waiting stage, will still run operations like pulling images or applying secrets, etc. To check the Waiting pod status, use the below command. kubectl describe pod [POD_NAME] Along with this state, a message and reason about the state are displayed to provide more information.... State: Waiting Reason: ErrImagePull ... 2. Running Pods – containers that are running without issues. The following command is executed before the pod enters the Running state.postStartRunning pods will display the time of the entrance of the container.... State: Running Started: Wed, 30 Jan 2019 16:46:38 +0530 ... 3. Terminated Pods – container, which fails or completes its execution; stands terminated. The following command is executed before the pod is moved to Terminated.prestopTerminated pods will display the time of the entrance of the container.... State: Terminated Reason: Completed Exit Code: 0 Started: Wed, 30 Jan 2019 11:45:26 +0530 Finished: Wed, 30 Jan 2019 11:45:26 +0530 ... Kubernetes’ self-healing Concepts – pod’s phase, probes, and restart policy.The pod phase in Kubernetes offers insight into the pod’s placement. We can havePending Pods – created but not runningRunning Pods – runs all the containersSucceeded Pods – successfully completed container lifecycleFailed Pods – minimum one container failed and all container terminatedUnknown PodsKubernetes execute liveliness and readiness probes for the Pods to check if they function as per the desired state. The liveliness probe will check a container for its running status. If a container fails the probe, Kubernetes will terminate it and create a new container in accordance with the restart policy. The readiness probe will check a container for its service request serving capabilities. If a container fails the probe, then Kubernetes will remove the IP address of the related pod.Liveliness probe example. apiVersion: v1 kind: Pod metadata: labels: test: liveness name: liveness-http spec: containers: - args: - /server image: k8s.gcr.io/liveness livenessProbe: httpGet: # when "host" is not defined, "PodIP" will be used # host: my-host # when "scheme" is not defined, "HTTP" scheme will be used. Only "HTTP" and "HTTPS" are allowed # scheme: HTTPS path: /healthz port: 8080 httpHeaders: - name: X-Custom-Header value: Awesome initialDelaySeconds: 15 timeoutSeconds: 1 name: liveness The probes includeExecAction – to execute commands in containers.TCPSocketAction – to implement a TCP check w.r.t to the IP address of a container.HTTPGetAction – to implement a HTTP Get check w.r.t to the IP address of a container.Each probe gives one of three results:Success: The Container passed the diagnostic.Failure: The Container failed the diagnostic.Unknown: The diagnostic failed, so no action should be taken.Demo description of Self-Healing Kubernetes – Example 1We need to set the code replication to trigger the self-healing capability of Kubernetes.Let’s see an example of the Nginx file. apiVersion: apps/v1 kind: Deployment metadata: name: nginx-deployment-sample spec: selector: matchLabels: app: nginx replicas:4 template: metadata: labels: app: nginx spec: containers: - name: nginx image: nginx:1.14.2 ports: - containerPort: 80 In the above code, we see that the total number of pods across the cluster must be 4.Let’s now deploy the file.kubectl apply nginx-deployment-sampleLet’s list the pods, usingkubectl get pods -l app=nginxHere is the output.NAME READY STATUS RESTARTS AGE nginx-deployment-test-83586599-r299i 1/1 Running 0 5s nginx-deployment-test-83586599-f299h 1/1 Running 0 5s nginx-deployment-test-83586599-a534k 1/1 Running 0 5s nginx-deployment-test-83586599-v389d 1/1 Running 0 5s As you see above, we have created 4 pods.Let’s delete one of the pods.kubectl delete nginx-deployment-test-83586599-r299iThe pod is now deleted. We get the following outputpod "deployment nginx-deployment-test-83586599-r299i" deletedNow again, list the pods.kubectl get pods -l app=nginxWe get the following output.NAME READY STATUS RESTARTS AGE nginx-deployment-test-83586599-u992j 1/1 Running 0 5s nginx-deployment-test-83586599-f299h 1/1 Running 0 5s nginx-deployment-test-83586599-a534k 1/1 Running 0 5s nginx-deployment-test-83586599-v389d 1/1 Running 0 5s We have 4 pods again, despite deleting one.Kubernetes has self-healed to create a new node and maintain the count to 4.Demo description of Self-Healing Kubernetes – Example 2Get pod details$ kubectl get pods -o wideGet first nginx pod and delete it – one of the nginx pods should be in ‘Terminating’ status$ NGINX_POD=$(kubectl get pods -l app=nginx --output=jsonpath="{.items[0].metadata.name}") $ kubectl delete pod $NGINX_POD; kubectl get pods -l app=nginx -o wide $ sleep 10 Get pod details – one nginx pod should be freshly started$ kubectl get pods -l app=nginx -o wideGet deployement details and check the events for recent changes$ kubectl describe deployment nginx-deploymentHalt one of the nodes (node2) $ vagrant halt node2 $ sleep 30 Get node details – node2 Status=NotReady$ kubectl get nodesGet pod details – everything looks fine – you need to wait 5 minutes$ kubectl get pods -o widePod will not be evicted until it is 5 minutes old – (see Tolerations in ‘describe pod’ ). It prevents Kubernetes to spin up the new containers when it is not necessary$ NGINX_POD=$(kubectl get pods -l app=nginx --output=jsonpath="{.items[0].metadata.name}") $ kubectl describe pod $NGINX_POD | grep -A1 Tolerations Sleeping for 5 minutes$ sleep 300Get pods details – Status=Unknown/NodeLost and new container was started$ kubectl get pods -o wideGet deployment details – again AVAILABLE=3/3$ kubectl get deployments -o widePower on the node2 node $ vagrant up node2 $ sleep 70 Get node details – node2 should be Ready again$ kubectl get nodesGet pods details – ‘Unknown’ pods were removed$ kubectl get pods -o wideSource: GitHub. Author: Petr RuzickaConclusionKubernetes can self-heal applications and containers, but what about healing itself when the nodes are down? For Kubernetes to continue self-healing, it needs a dedicated set of infrastructure, with access to self-healing nodes all the time. The infrastructure must be driven by automation and powered by predictive analytics to preempt and fix issues beforehand. The bottom line is that at any given point in time, the infrastructure nodes should maintain the required count for uninterrupted services.Reference: kubernetes.io, GitHub

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