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2 Approaches to Ensure Success in Your DevOps Journey

DevOps makes continuous software delivery simple for both development and operation teams, with a set of tools and best practices. In order to understand the power of DevOps, we chose a standard development environment with a suite of applications such as git, Gerrit, Jenkins, JIRA and Nagios. We studied setting up such a traditional environment and compared the same with a more modern approach involving docker containers. Introduction In this article we will discuss about DevOps, traditional and container based ways of approaching it. For this purpose, we will have a fictitious software company (client) which wants to streamline its development and delivery process. What is DevOps? DevOps means many things to many people. The one that is closest to our view is “DevOps is the practice of operations and development engineers participating together in the entire service lifecycle, from design through the development process to production support”. The process of continuous delivery of good quality code with the help of tools that make it easy. There are many tools that work in tandem to ensure that only good quality code ends up to the production. Our client wants to use the following tools. DevOps Tools Git – Most popular distributed version control system. Gerrit – Code review tool. Jenkins -Continuous integration tool. JIRA – Bug tracking tool. Development Workflow We came up with the following workflow that captures a typical development life cycle: A developer will commit their changes to the staging area of a branch. Gerrit will watch for commits in the staging area. Jenkins will watch Gerrit for new change sets to review. It will then trigger a set of jobs to run on the patch set. The results are shared with both Gerrit and JIRA. Based on the commit message the appropriate JIRA issue will be updated. When reviewers accept the change, it will be ready to commit. In order to let Jenkins auto update JIRA issue, a pattern was enforced for all commits. This allowed us to automate Jenkins to find and update a specific JIRA issue. DevOps Operations Workflow Operation teams were more concerned with provisioning machines (physical or virtual), installing suite of applications and eventually monitoring those machines and applications. Alert notifications are important too, in order to address any anomalies at the earliest. For monitoring and alert notification we used Nagios. Two Types of DevOps Approach Traditional Approach The traditional approach is to manually install these tools on bare metal boxes or virtual machines and configuring them to talk to each other. Following are the brief steps for traditional DevOps infrastructure. Git/Gerrit, Jenkins, JIRA is installed on single or multiple machines. Gerrit project,accounts are created and access is provided as per the requirement. Required plugins are installed on Jenkins( i.e. Gerrit-trigger, Git client, JIRA issue updater, Git etc). Previously installed plugins are configured on Jenkins. Jenkins ssh key is added against Gerrit account. Multiple accounts with few issues are created in JIRA. Now the whole DevOps infrastructure is ready to be used. DevOps Automation Services via Python Script We automated the workflow for both installation, configuration and monitoring using python script. Actual python code for downloading, installing and configuring Git, Gerrit, Jenkins, JIRA and Nagios can be found in the following Github repository. https://github.com/sujauddinmullick/dev_ops_traditional Container Approach The automation of installation and configuration relieves us of some pain in setting up infrastructure. Think of a situation where the client’s environment dependencies conflict with our DevOps infrastructure dependencies. In order to solve this problem, we tried isolating the DevOps environment from existing environment. We used docker engine to setup these tools. A docker engine builds over a linux container. A linux container is an operating system-level virtualization method for running multiple isolated linux systems (continers) on a single control host[2]. It differs from virtual machines in many ways. One stricking difference is containers share host’s kernel and library files but virtual machines do not. This makes containers lightweight than virtual machines. Getting a linux container up and running is once again a difficult task. Docker makes it simple. Docker is built on top of Linux containers, which makes it easy to create, deploy and run applications using containers. Dockerfile is used to create a container image. It contains instructions for docker to assemble an image. For example, following Dockerfile will build a Jenkins image. Sample Dockerfile for Jenkins: FROM jenkins MAINTAINER sujauddin # Install plugins COPY plugins.txt /usr/local/etc/plugins.txt RUN /usr/local/bin/plugins.sh /usr/local/etc/plugins.txt # Add gerrit-trigger plugin config file COPY gerrit-trigger.xml /usr/local/etc/gerrit-trigger.xml COPY gerrit-trigger.xml /var/jenkins_home/gerrit-trigger.xml # Add Jenkins URL and system admin e-mail config file COPY jenkins.model.JenkinsLocationConfiguration.xml /usr/local/etc/jenkins.model.JenkinsLocationConfiguration.xml COPY hudson.plugins.JIRA.JIRAProjectProperty.xml /var/jenkins_home/hudson.plugins.JIRA.JIRAProjectProperty.xml COPY jenkins.model.JenkinsLocationConfiguration.xml /var/jenkins_home/jenkins.model.JenkinsLocationConfiguration.xml #COPY jenkins.model.ArtifactManagerConfiguration.xml /var/jenkins_home/jenkins.model.ArtifactManagerConfiguration.xml # Add setup script. COPY jenkins-setup.sh /usr/local/bin/jenkins-setup.sh # Add cloud setting in config file. COPY config.xml /usr/local/etc/config.xml COPY jenkins-cli.jar /usr/local/etc/jenkins-cli.jar COPY jenkins_job.xml /usr/local/etc/jenkins_job.xml We can run the previously built images inside a docker container. To setup and run a set of containers, docker-compose tool is used. This command will take a docker-compose.yml file and builds and runs all the containers defined there. The actual compose file we used to build git, gerrit, jenkins and JIRA is given below. docker-compose.yml final_gerrit: image: sujauddin/docker_gerrit_final restart: always ports: - 8020:8080 - 29418:29418 final_JIRA: build: ./docker-JIRA ports: - 8025:8080 restart: always final_jenkins: build: ./docker-jenkins restart: always ports: - 8023:8080 - 8024:50000 links: - final_JIRA - final_gerrit final_DevOpsnagios: image: tpires/nagios ports: - 8036:80 - 8037:5666 - 8038:2266 restart: always With one command we got all the containers up and running with all the necessary configurations done so that the whole DevOps workflow runs smoothly. Isn’t that cool? Conclusion Clearly docker based approach is easy to setup and more efficient. Containers can be quickly deployed (usually in a few seconds), can be ported along with the application and its dependencies and has minimal memory footprint. Resulting into a happy client!

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devops docker

5 DevSecOps Best Practices for Your Security Team

Pamela, Product Head of an ISV, envisions the transformation of her team’s Dev and Ops processes. Pamela establishes a DevOps team to facilitate ‘continuous everything.’ She intends to achieve unmatched product quality, process automation, and risk-averse digital infrastructure. Six months down the line – her team witnessed a faster development cycle. But Pamela isn’t satisfied. This is because, in the last six months, a couple of security incidents have been reported. After investigation, the cause was identified as undetected bugs, which were there right from the coding environment. Well, the fact remains that Pamela and her team aren’t only one to suffer. Per the 2019 Sonatype DevSecOps survey, every one in four companies has experienced a breach in 2018-2019. DevOps Mantra – Make Security its Core and not just a Preservative It is awesome how DevOps automates development, production, testing, and deployment environment. However, the automation chain often ignores the essential security protocols. Therefore, data, which is left unencrypted in the development environment, becomes an easy target for breaches. So, the key is to integrate security right at an earlier stage. When practicing DevOps, there are multiple changes in codes in less time. The speed often outdoes the security team’s efforts and leaves them flat-footed. This poor alignment between teams results in a lack of security disciplines – unplanned vulnerabilities, less robust codes, insecure passwords, to name a few. The Sonatype survey states that 48 percent of respondents admitted lack of time for not practicing security at an early stage of the SDCL lifecycle. An interesting thing to note is that this number hasn’t gone down since 2018. Honestly, DevSecOps completes the DevOps lifecycle by injecting security into its core. It helps companies transcend into a broader security blanket with source code analysis, vulnerability testing, penetration testing, and access management, among others. However, having in place a DevSecOps guide has been a matter of concern. Let us analyze the top two challenges experienced by organizations in implementing DevSecOps. People Neutralizing corporate mindsets to accepting the change is like untying an intricate knot. You need to bring the team on one page and show them the bigger picture. Make them realize the long-term benefits of practicing security since inception. The Sonatype survey says that only one in four respondents believe that safety and quality run parallel. Expertise A 2018-2019 survey, which was based on DevOps, showed that 58 percent of tech leaders think lack of skills hinders the embedment of security and testing within the SDCL. Lack of expertise will make the complete DevSecOps plan vulnerable. What to do is essential, but how to do is the key. Often organizations lack the skills to design an effective DevSecOps plan with defined milestones, clear operative procedures, and deliverables and project owners. Mapping DevSecOps process flow within an organization and ensuring its success requires the right mix of tools, policies, methodologies, and practices. The bottom-line remains smooth synchronization between Dev, Ops, and the Infosec team. So, let us now look at the five-pointer DevSecOps security checklist that can be included as DevSecOps best practices. 1 Embrace Automation The standard requirement for continuous testing and continuous integration is speed, which makes automation a fundamental requirement. Therefore, having essential security controls and trigger points is essential. Per the Sonatype 2019 survey, 63 percent of the respondents said to have automated their security practices. Further, it is also vital to have mindful automation is place. For example, your source code scan need not be done for the whole application daily. It can be confined to the daily codes committed only. Also, the key is to have not only static application security testing but also include dynamic application security testing. This way, we will ensure vulnerability scanning in real-time. It is equally important to have a relevant and optimal set of tools that will infuse automation to your configuration management, code analysis, patching, and access management. 2 Risk Management of Third-Party Tools & Technologies The use of open source technologies for application development is on the rise. Per the 2019 Red Hat report, 69% of respondents believe that open source technology is crucial. However, there are security concerns around the use of open source technologies that must be addressed. The Red Hat report cites – “Security is still cited as an open-source concern. Some of that fear likely stems from general security concerns since hacks and data breaches seem to be daily news. This concern may also reflect how unmanaged open source code—found across the web or brought in through dependencies—can introduce vulnerabilities in both open source and proprietary solutions.” Developers are too busy to review open-source codes. This might bring unidentified vulnerabilities and other security issues on the codes. Therefore, code dependency testing is necessary. Having an OWASP utility check will ensure that there is no vulnerability in codes, which are dependent on open-source components. 3 Uniform Security Management Process The security team will usually post the bugs report in different bug repositories. Developers don’t have the bandwidth to check all the reports. And top of it, multiple priorities result in precedence to functional testing over security issues. Therefore, it is fundamental to DevSecOps to have in place a uniform security application management system. This way any modification in codes is reflected in one place. The security team is also immediately notified of executing the authentication-testing protocol. Another critical point is to follow the ‘secure by design’ principle via the automation of security tasks. This helps to create and maintain collective software and security elements like correct authorization, control mechanisms, audit management, and safety protocol. Resultant – a transparent security culture. 4 Integrating Application Security System with Bugs Tracker The application security system should be integrated with your task management system. This will create a list of bugs tasks automatically that can be executed by the infosec team. Additionally, it will provide actionable details such as the nature of the bug, its severity and treatment required. Thus, the security team becomes empowered to fix issues before they land to the production and deployment environment. 5 Threat Modeling – The Last Key The SANS Institute advocates risk assessment before implementing DevSecOps methodology. Following threat modeling will result in risk-gap analysis – helping you identify software components, which are under threats, level of threats, and possible solutions to counter those threats. In fact, with threat modeling, the development team is equipped to locate fundamental glitches in the architecture. This way they can make necessary changes in application designs. Conclusion The ferocious rise in the competition demand reduction in time-to-market of the application. This must be supplemented with superior quality. Therefore, DevOps as a practice is only expected to increase. Rendering DevSecOps services for a while now, we have realized that imbibing security right from the early stages is only the key to maintain zero deployment downtime. Organizations must be thoughtful while shifting to Dev + Security + Operations. They should follow the idea of the People>Process>Technology. And, while doing so, the above 5 DevSecOps best practices will lay the foundation.

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devops DevSecOps security

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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ci/cd devops Microservices

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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devops DevOps Security DevSecOps

5 Ways How DevOps Becomes a Dealmaker in Digital Transformation

The culture of DevOps-ism is a triumph for companies. DevOps has plundered the inefficiencies of the traditional model of software product release. But, there is a key to it. Companies must unlock the true DevOps tenacity by wiring it with its primary stakeholders – People and Process. A recent survey shows that most teams don’t have a flair for DevOps implementation. Another study reveals that around 78 percent of the organizations fail to implement DevOps. So, what makes the difference? Companies must underline and acclimatize the cultural shift, which erupts with DevOps. This culture is predominantly driven by automation to empower resilience, reduce costs and accelerate innovation. The atoms that make up the cultural ecosystem are people and processes. Funny story, most companies that dream of being digital savvy, still carry primitive mind-sets. Some companies have recognized this change. The question remains – are they adept at pulling things together? Are You in the Pre-DevOps Era, Still? It is archaic! Collaboration and innovation, for the most part, is theoretical. The technological proliferation coupled with cut-throat competition has put your company in a hotspot. You feel crippled embracing the disruptive wave of the digital renaissance. Also, you feel threatened by a maverick Independent Software Vendor – who is new to the software sector. If the factors above seem, relevant, it is time to move away from the legacy approach. The idea is simple – streamline and automate your software production – across the enterprise. It is similar to creating assembly lines, which operates parallel, continuous and in real-time. If you consider manufacturing, this concept is more than 150 years old. In software space, we have just realized the noble idea. Where it all started….. The IT industry experienced a radical change due to rapid consumerization and technological disruption. This created a need for companies to be more agile, intuitive and transparent in their service offerings. The digital transformation initiatives are continually pushing the boundaries to deliver convergent experiences that are insightful, social and informative. Further, the millennials who form more than 50 percent part of the overall IT decision makers globally are non-receptive to inefficient technologies and slow processes. They want their employees to work in an innovative business environment with augmented collaboration and intelligent operations. It is essential for the organization to follow an integrated approach for driving digital transformation, integrating cross-functionalities and enabling IT agility. DevOps enables enterprises to design, create, deploy and manage applications with new age software delivery principles. It also helps in creating unmatched competencies for delivering high-quality applications faster and easier; while accelerating innovation. With DevOps, organizations can divide silos facilitating collaboration, communication, and automation with better quality and reduced risk and cost. Below are the five key DevOps factors to implement for improving efficiency and accelerating innovation. 1. Automating Continuous Integration/Continuous Delivery DevOps is not confined to your departments. Nor it is just a deployment of some five-star tools. DevOps is a journey to transform your organization. It is essential to implement and assess a DevOps strategy to realize the dream of software automation. Breaking the silos, connecting isolated teams and wielding a robust interface can become taskmasters. This gets more tedious for larger companies. The initial focus must remain on integrating people in this DevOps model. The idea is to neutralize resistance, infuse confidence, and empower collaboration. Once these ideas become a reality, automation will become the protagonist. The question remains – How automation will be the game changer? This brings the lens on Continuous Integration/ Continuous Delivery (CI/CD). It works as a catalyst in channelizing automation throughout your organization. Historically, software development and delivery have been teeth-grinding. Even the traditional DevOps entails a manual cycle of writing codes, conducting tests, and deploying codes. This brings several pitfalls – multiple touchpoints, non-singular monitoring, increased dependencies on various tools, etc. How to Automate the CI/CD Pipeline? Select an automation server that provides numerous tools and interfaces for automation Select a version control and software development platform to commit codes Pull the codes in the build phase via automation server Compile codes in the build phase for various tasks Execute a series of tests for the compiled codes Release the codes in the staging environment Deploy the codes from the staging server via Docker An automated CI/CD pipeline will mitigate caveats associated with the traditional DevOps. It will result in a single, centralized view of project status, across stages. It drastically brings down the human intervention, moving you towards zero errors. But, is that all simple? Definitely no. It has its own set of challenges. Companies that are maneuvering from waterfall to DevOps, often end up automating wrong processes. How can teams avoid this? Well, have the following checklist handy. The frequency of process/workflow repetitions The time duration of the process Dependencies on people, tools, and technologies Delays resulting due to dependencies Errors in processes, if it is not automated These checklists will provide insights on the bottlenecks. It will help prioritize and automate critical tasks – starting from code compiling, testing to deployment. 2. The Holy Nexus of Cloud and DevOps You don’t buy a superbike to drive it in city traffics. You would prefer wide roads, less traffic to unleash its true speed. Then why do Cloud without DevOps? The combination of Cloud and DevOps is magical. Often, IT managers don’t realize it. Becoming a Cloud first company is not possible without a DevOps first approach. It is a case of the sum being more significant than parts. What is the point of implementing DevOps correctly, when the deployment platform is inefficient? Similarly, a scalable deployment platform loses its charm without fast and continuous software development. Cloud creates a single ecosystem, which provides DevOps with its natural playground. The centralized platform offered by Cloud enables continuous production, testing, and deployment. Most Cloud platforms come with DevOps capabilities of Continuous Integration and Continuous Delivery. This reduces the cost of DevOps in an On-Premise environment. Consider the case of Equifax – a consumer credit reporting company. They store their data on cloud and in-house data centers. In 2018, they released a document on the cyber-attack, which hit them in Sep 2017. Hackers collected around 2.4 million personally identifiable information (PII) of their customers. The company had to announce that they will provide credit file monitoring services to affected customers at no cost. Isn’t it damaging – monetarily and morally? But, what made hackers get access to such sensitive customer information? Well, per the website, there was a vulnerability Apache Struts CVE-2017-5638 to steal the data. Although the company patched this vulnerability in March 2017, it required more profound expertise and smarter process regime. If they had a DevOps strategy to redeploy software with continuous penetration testing more frequently, a cyber-attack could have averted. It is a genuine concern for any CIO to derive the value of cost, agility, security, and automation from their Cloud investment. The most common hurdle to this is the less compatible IT process. There other significant challenges too. Per a recent survey by RightScale, around 58 percent of Cloud users think saving cost is their top priority. Approximately 73 percent of the respondents believe that lack of skill expertise is a significant challenge. More than 70 percent of respondent said that governance and quality is an issue. The report also outlines integration as a challenge when moving from a legacy application to the Cloud. DevOps can standardize the processes and set the right course to leverage Cloud. DevOps in the backend and Cloud in the frontend gives a competitive edge. Cloud works well when your Infrastructure as Code (IaC) is successful. IT teams must write the right scripts and configure it in the application. Manually writing infrastructure scripts can be daunting. DevOps can automate scripts for aligning IT processes to Cloud. 3. Microservices – The Modern Architecture Microservices Without DevOps? Think Again! The sea-changes in consumer preferences have altered companies’ approach to delivering applications. Consumers want results in real-time, unique to their needs. Perhaps, this is why 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. 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. Leveraging Microservices with a waterfall approach will not extract its real benefits. You must de-couple the silo approach to incubate the gems of DevOps – People>Process>Automation. Microservices without DevOps would severely jolt teams productivity. The Quality Assurance teams would experience neck-breaking pressure due to untested codes. They will become bottlenecks, hampering the process efficiencies. DevOps with its capability to trigger continuity will stitch every workflow through automation. 4. Containers –Without DevOps? Consider companies of the size and nature of Netflix that require to update data in real-time and on an on-going basis. They must keep their customers updated with new features and capabilities. This isn’t feasible without Cloud. And, on top of that, releasing multiple changes daily, will be dreadful. Thereby, for smooth product operations, Container Architecture is a must. In such a case, they must daily update their Container Services – multiple times. It entails website maintenance, releasing new services (in different locations) and responding to security threats. Even if you are a small to medium Independent Software Vendor operating in the upper echelons of the technology world, your software product requires a daily upbeat. Your developers will always be on their toes for daily security and patching updates. This a daunting task, isn’t it? DevOps is the savior. DevOps will hold back for your applications that are built in the Cloud. It will set a continuous course of monitoring through automation and ease the pressure of monitoring from developers. Without DevOps, Container Architecture won’t sustain the pressure. 5. Marrying DevOps, Lean IT, and Agile The right mix of DevOps, Lean and Agile amplifies business performance. Agile emphasizes greater collaboration for developing software. Lean focuses on eliminating wastes. DevOps wants to align software development with software delivery. The three work as positives; adding them will only augment the outcome. However, there persists a contradiction in perception towards adopting these three principles. When Agile took strides, the teams said that we already do Lean IT. When DevOps took strides, the teams said that we already do Agile. But, the three principles strive to achieve similar things in different areas of the software lifecycle. Combining DevOps, Lean and Agile can be an uphill task. Especially, for leaders that carry the traditional mindset. Organizations must revive their leadership style to align with modern business practices. The aim must be moving towards a collaborative environment for delivering value to the customers. Companies must focus on implementing a modern communication strategy at the workplace. It is necessary that they address the gaps between IT and the rest of the groups within an organization. They must be proactive in initiating mindful cross-functional relationships, backed by streamlined communications. The software development teams will then work as protagonists in embracing DevOps, Lean and Agile to survive the onslaught of competition. It is also essential to champion each of the above concept. This will ensure that we profit out of each component in the combination. Organizational leadership must relentlessly work to create a seamless workflow, while removing bottlenecks, cutting delays, and eliminating reworks. Companies haven’t yet fathomed the true benefits of DevOps-Agile-Lean combination. It needs time and the team of experts to capitalize on these three principles. Additionally, companies shy away from exploiting the agility and responsiveness of modern delivery architects – Microservices, in particular. This becomes a hindrance in reaping the full potential of the combination. The crux of driving DevOps-Agile-Lean combination is a business-driven approach. Continual feedback backed by the right analytics also plays a crucial role. It facilitates fail-fast, thereby, creating a loop of continuous improvement. Agile offers a robust platform to design software, which is tuned with the market demands. DevOps stitches the process, people and technology, ensuring efficient software delivery. Final Thoughts Adopting DevOps is a promising move. Above, we have depicted in 5 manners how DevOps is your digital transformation dealmaker. However, it can be nerve crunching. It takes patience, expertise, and experience for embodying its purest form. A half-baked DevOps strategy might give you a few immediate results. In the long run, it will deride your teams’ efforts. However, automation is the best way to sail through it.

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ci/cd cloud cloud devops Containers continuous integration continuous-delivery devops Digital Transformation Microservices

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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AI devops DevOps testing

9 Best Practices for a Mature Continuous Delivery Pipeline

Continuous Integration(CI) is a software engineering practice which evolved to support extreme and agile programming methodologies. CI consists of best practices consisting of build automation, continuous testing and code quality analysis. The desired result is that software in mainline can be rapidly built and deployed to production at any point. Continuous Delivery(CD) goes further and automates the deployment of software in QA, pre-production and production environments. Continuous Delivery enables organizations to make predictable releases reducing risk and, automation across the pipeline enables reduction of release cycles. CD is no longer an option if you run geographically distributed agile teams. Aziro (formerly MSys Technologies) has designed and deployed continuous integration and delivery pipelines for start-ups and large organizations leading to benefits like: Automate entire pipeline – reduce manual effort and accelerate release cycles Improve release quality – reduce rollbacks and defects Increased visibility leading to accountability and process improvements Cross-team visibility and openness – increased collaboration between development, QA, support and operations teams Reduction in costs for deployment and support A mature continuous delivery pipeline consists of the following steps and principles: 1. Maintain a single code repository for the product or organization Revision control for the project source code is absolutely mandatory. All the dependencies and artifacts required for the project should be in this repository. Avoid branches per developer to foster shared ownership and reduce integration defects. Git is a popular distributed version control system that we recommend. 2. Automated builds Leverage popular build tools like ANT, make, maven, etc to standardize the build process. A single command should be capable of building your entire system including the binaries and distribution media (RPM, tarballs, MSI files, ISOs). Builds should be fast – larger builds can be broken into smaller jobs and run in parallel. 3. Automated testing for each commit An automated process where each commit is built and tested is necessary to ensure a stable baseline. A continuous integration server can monitor the version control system and automatically run the builds and tests. Ideally, you should hook up the continuous integration server with Gerrit or ReviewBoard to report the results to reviewers. 4. Static Code Analysis Many teams ignore code quality until it is too late and accumulate heavy technical debt. All continuous integration servers have plugins that enable integration of static code analysis within your CD pipeline or one can also automate this using custom scripts. You should fail builds that do not pass agreed upon code quality criteria. 5. Frequent commits into baseline Developers should commit their changes frequently into the baseline. This allows fast feedback from the automated system and there are fewer conflicts and bugs during merges. With automated testing of each commit, developers will know the real-time state of their code. Integration testing in environment that are production clones Testing should be done in an environment that is as close to production as possible. The operating system versions, patches, libraries and dependencies should be the same on the test servers as on the production servers. Configuration management tools like Chef, Puppet, Ansible should be used to automate and standardize the setup of environments. 6. Well-defined promotion process and managing release artifacts Create and document a promotion process for your builds and releases. This involves defining when a build is ready for QA or pre-production testing. Or which build should be given to the support team. Having a well-defined process setup in your continuous integration servers improves agility within disparate or geographically distributed teams. Most continuous integration servers have features that allow you to setup promotion processes. Large teams tend to have hundreds or thousands of release artifacts across versions, custom builds for specific clients, RC releases, etc. A tool like Nexus or Artifactory can be used to efficiently and predictably store and manage release artifacts. 7. Automate your Deployment An effective CI/CD pipeline is one that is fully automated. Automating deployments is critical to reduce wastage of time and avoid possibility of human errors during deployment. Teams should implement scripts to deploy builds and verify using automated tests that the build is stable. This way not only the code but the deployment mechanisms also get tested regularly. It is also possible to setup continuous deployment which includes automated deployments into production environments along with necessary checks and balances. 8. Configuration management for deployments Software stacks have become complicated over the years and deployments more so. Customers commonly use virtualized environments, cloud and multiple datacenters. It is imperative to use configuration management tools like Chef, Puppet or custom scripts to ensure that you can stand up environments predictably for dev, QA, pre-prod and production. These tools will also enable you to setup and manage multi-datacenter or hybrid environments for your products. 9. Build status and test results should be published across the team Developers should be automatically notified when a build breaks so it can be fixed immediately. It should be possible to see whose changes broke the build or test cases. This feedback can be positively used by developers and QA to improve processes. Every CxO and engineering leader is looking to increase the ROI and predictability of their engineering teams. It is proven that these DevOps and Continuous Delivery(CD) practices lead to faster release cycles, better code quality, reduced engineering costs and enhanced collaboration between teams. Learn more about Aziro (formerly MSys Technologies)’ skills and expertise in DevOps/CI/Automation here. Get in touch with us for a free consulting session to embark on your journey to a mature continuous delivery pipeline – email us at marketing@aziro.com

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AIOps and the Future of SRE 2022: How Modernized DevOps Automation Services Lead The Way for Site Reliability

Right from its early days Site Reliability Engineering has been inseparable from DevOps automation services for automating IT operations tasks like production system management, change management, incident response, and even emergency response. Still, even the most experienced SRE teams have issues, particularly with the massive amounts of data generated by hybrid cloud and cloud-native technologies. This problem extends itself to DevOps performance because the challenge is to increase the stability, dependability, and availability of SRE models in real-time across different systems. This means that if the SRE-ship is sinking, the DevOps is coming along too. Unless there is something about DevOps that can change the waters altogether. SRE teams are looking toward more intelligent IT operations to assist them to solve the issues mentioned above. A possible candidate for this purpose can be AIOps. AI-based specialized DevOps can aid SRE with intelligent incident management and resolution. AI and machine learning (ML) have emerged to allow teams to focus on high-value work and innovation by reducing the manual work associated with the demanding SRE function. AIOps automates IT operations activities such as event correlation, anomaly detection, and causality determination by combining big data and machine learning. So it would be interesting to look at the possibility of AIOps and SRE coming together for a better DevOps performance. A Quick AIOps Overview Though the advances in AIOps constitute a separate discussion of their own. We, too, have talked about the role of AI in modern DevOps machinery. But for the sake of our existing discussion, we will focus on three crucial aspects of AIOps. Increased Service Levels: AIOps can improve service levels with the help of predictive insights and comprehensive orchestration. Teams can enhance the user experience by reducing the time spent evaluating and resolving issues. Boost In Operational Efficiency: Because manual activities are removed, procedures are optimized, and cooperation across the SDLC cycle is improved, operational efficiency gets a major push in AI-based DevOps Improved Scalability and Agility: By using AIOps to set up automation and visualization, you may gain insights into how to increase the scalability of your software and your SDLC team. It will also improve the agility and speed of your DevOps initiatives as a result. So how do these benefits work in favor of SRE Modernization? Automation is the most valuable aspect of AIOps. SRE can provide continuous and comprehensive service because of automation. It shortens the lifetime by reducing the number of stages in processes. Therefore, it is the automation part where SRE and AIOps can find their common grounds and help the DevOps save time and focus on more critical responsibilities. The Need for AIOps for SRE According to SRE, IT teams should always keep a check on IT outages, and crises are proactively resolved before they have an impact on the user. Even the most experienced SRE teams have issues; teams are accountable for dynamic and complex applications, often across multiple cloud environments. While executing these activities in real-time, SRE confronts obstacles such as lack of visibility and technological fragmentation. This is where AIOps fits into the puzzle. AIOps make proactive monitoring and issue management possible. If AIOps tools can warn SREs of developing concerns before they become actual incidents, AIOps can assist SREs in getting ahead of issues before they become real incidents. That benefits both SREs and end-users. There is also a case that AIOps may assist SREs in getting more done with less technical staff. You can keep the same levels of availability and performance with fewer human engineers on hand if you can utilize AI to automate some elements of monitoring and problem response. Understanding the Working of AIOps and SRE Many SRE teams have already begun using AI skills to find and analyze trends in data, remove noise, and derive valuable insights from current and historical data. As AIOps moves into the area of SRE, it has made issue management and resolution faster and more automated. SRE teams can now devote their attention to strategic projects and focus on delivering high value to consumers. Analyze Datasets Topology Analytics is used by AIOps to collect and correlate information. In general, underlying causes are difficult to locate. AIOps automatically detects and resolves the fundamental causes of problems. In comparison to this technique, manual identifying and correcting is inefficient. Delivery Chain Visibility The supply chain is visible, so teams can see what they’re doing and what they need to accomplish. AIOps depicts two aspects of an organization. The user experience is the first. SRE can improve the end-user experience by leveraging AIOps’ automation and predictive analytics. The network and application performance is the second factor to consider. Network and application performance is improved by eliminating manual chores, boosting cooperation, and automating processes. Categorized and Minimized Noises The goal of SRE is to increase user engagement with the app. The typical monitoring method is inefficient and prone to false alarms. Machine learning is used by AIOps to detect and prioritize alarms. AIOps auto-fixes issues in some circumstances. As a result, SRE teams may concentrate on tackling just the most significant issues. Conclusion: The SRE benefits from AIOps because it integrates autonomous diagnostics and metric-driven continuous improvement for development and operations throughout the SDLC. AIOps boost service levels and enhance teams’ efficiency, scalability, and agility. Continuous improvement builds confidence in SRE members. Adopting SRE and AIOps together allows organizations to achieve their goals smoothly. As a result, there are more chances and time to focus on excellent people and innovative projects that provide more value to users.

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AIOps devops SRE

Aziro (formerly MSys Technologies) 2019 Tech Predictions: Smart Storage, Cloud’s Bull Run, Ubiquitous DevOps, and Glass-Box AI

2019 brings us to the second-last leg of this decade. From the last few years, IT professionals have been propagating rhetoric. They state that the technology landscape is seeing a revolutionary change. But, most of the “REVOLUTIONARY” changes, has, over the time lost their gullibility. Thanks to the awe-inspiring technologies like AI, Robotics, and upcoming 5G networks most tech pundits consider this decade to be a game changer in the technology sector.As we make headway into 2019, the internet is bombarded with numerous tech prophecies. Aziro (formerly MSys Technologies) presents to you the 2019 tech predictions based on our Storage, Cloud, DevOps and digital transformation expertise.1. Software Defined Storage (SDS)Definitely, 2019 looks promising for Software Defined Storage. It’ll be driven by changes in Autonomous Storage, Object Storage, Self-Managed DRaaS and NVMes. But, SDS will also be required to push the envelope to acclimatize and evolve. Let’s understand why so.1.1 Autonomous Storage to Garner MomentumBacked by users’ demand, we’ll witness the growth of self-healing storage in 2019. Here, Artificial Intelligence powered by intelligent algorithms will play a pivotal role. Consequently, companies will strive to ensure uninterrupted application performance, round the clock.1.2 Self-Managed Disaster Recovery as a Service (DRaaS) will be ProminentSelf-Managed DRaaS reduces human interference and proactively recovers business-critical data. It then duplicates the data in the Cloud. This brings relief during an unforeseen event. Ultimately, it cuts costs. In 2019, this’ll strike chords with enterprises, globally, and we’ll witness DRaaS gaining prominence.1.3 The Pendulum will Swing Back to Object Storage as a Service (STaaS)Object Storage makes a perfect case for cost-effective storage. Its flat structure creates a scale-out architecture and induces Cloud compatibility. It also assigns unique Metadata and ID for each object within storage. This accelerates the data retrieval and recovery process. Thus, in 2019, we expect companies to embrace Object Storage to support their Big data needs.1.4 NMVes Adoption to Register TractionIn 2019, Software Defined Storage will accelerate the adoption rate of NVMes. It rubs off glitches associated with traditional storage to ensure smooth data migration while adopting NVMes. With SDS, enterprises need not worry about the ‘Rip and Replace’ hardware procedure. We’ll see vendors design storage platforms that append to NVMes protocol. For 2019, NMVes growth will mostly be led by FC-NVME and NVMe-oF.2. Hyperconverged Infrastructure (HCI)In 2019, HCI will remain the trump card to create a multi-layer infrastructure with centralized management. We’ll see more companies utilize HCI to deploy applications quickly. This’ll circle around a policy-based and data-centric architecture.3. Hybridconverged Infrastructure will Mark its FootprintHybridconverged Infrastructure (HCI.2) comes with all the features of its big brother – Hyperconverged Infrastructure (HCI.1). But, one extended functionality makes the latter smarter. Unlike HCI.1, it allows connecting with an external host. This’ll help HCI.2 mark its footprint in 2019.4. VirtualizationIn 2019, Virtualization’s growth will be centered around Software Defined Data Centers and Containers.4.1 ContainersContainer technology is ace in the hole to deliver promises of multi-cloud – cost efficacy, operational simplicity, and team productivity. Per IDC, 76 percent of users’ leverage containers for mission-critical applications.4.1.1 Persistent Storage will be a Key ConcernIn 2019, Containers’ users will envision a cloud-ready persistent storage platform with flash arrays. They’ll expect their storage service providers to implement synchronous mirroring, CDP – continuous data protection and auto-tiering.4.1.2 Kubernetes Explosion is ImminentThe upcoming Kubernetes version is rumored to include a pre-defined configuration template. If true, it’ll enable an easier Kubernetes deployment and use. This year, we are also expecting a higher number of Kubernetes and containers synchronization. This’ll make Kubernetes’ security a burgeoning concern. So, in 2019, we should expect stringent security protocols around Kubernetes deployment. It can be multi-step authentication or encryption at the cluster level.4.1.3 Istio to Ease Kubernetes Deployment HeadacheIstio is an open source service mesh. It addresses the Microservices’ application deployment challenges like failure recovery, load balancing, rate limiting, A/B testing, and canary testing. In 2019, companies might combine Istio and Kubernetes. This can facilitate a smooth Container orchestration, resulting in an effortless application and data migration.4.2 Software Defined Data CentersMore companies will embark on their journey to Multi-Cloud and Hybrid-Cloud. They’ll expect a seamless migration of existing applications to a heterogeneous Cloud environment. As a result, SDDC will undergo a strategic bent to accommodate the new Cloud requirements.In 2019, companies will start cobbling DevOps and SDDC. The pursuit of DevOps in SDDC will be to instigate a revamp of COBIT and ITIL practice. Frankly, without wielding DevOps, cloud-based SDDC will remain in a vacuum.5. DevOpsIn 2019, companies will implement a programmatic DevOps approach to accelerate the development and deployment of software products. Per this survey, DevOps enabled 46x code deployment. It also skyrocketed the deploy lead time by 2556x. This year, AI/ML, Automation, and FaaS will orchestrate changes to DevOps.5.1 DevOps Practice Will Experience a Spur with AI/MLIn 2019, AI/ML centric applications will experience an upsurge. Data science teams will leverage DevOps to unify complex operations across the application lifecycle. They’ll also look to automate the workflow pipeline – to rebuild, retest and redeploy, concurrently.5.2 DevOps will Add Value to Functions as a Service (FaaS)Functions as a Service aims to achieve serverless architecture. It leads to a hassle-free application development without perturbing companies to handle the monolithic REST server. It is like a panacea moment for developers.Hitherto, FaaS hasn’t achieved a full-fledged status. Although FaaS is inherently scalable, selecting wrong user cases will increase the bills. Thus, in 2019, we’ll see companies leveraging DevOps to fathom productive user cases and bring down costs drastically.5.3 Automation will be the Mainstream in DevOpsManual DevOps is time-consuming, less efficient, and error-prone. As a result, in 2019, CI/CD automation will become central in the DevOps practice. Consequently, Infrastructure as a Code to be in the driving seat.6. Cloud’s Bull Run to ContinueIn 2019, organizations will reimagine the use of Cloud. There will be a new class of ‘born-in-cloud’ start-ups, that will extract more value by intelligent Cloud operations. This will be centered around Multi-Cloud, Cloud Interoperability, and High Performance Computing. More companies will look to establish a Cloud Center of Excellence (CoE). Per RightScale survey, 57 percent of enterprises already have a Cloud Center of Excellence.6.1 Companies will Drift from “One-Cloud Approach.”In 2018, companies realized that having a ‘One-Cloud Approach’ encumbers their competitiveness. In 2019, Cloud leadership teams will bask upon the Hybrid-Cloud Architecture. Hybrid-Cloud will be the new normal within Cloud Computing in 2019.6.2 Cloud Interoperability will be a Major ConcernIn 2019, companies will start addressing the issues of interoperability by standardizing Cloud architecture. The use of the Application Programming Interface (APIs) will also accelerate. APIs will be the key to instill the capability of language neutrality, which augments system portability.6.3 High Performance Computing (HPC) will Get its Place on CloudIndustries such as Finance, Deep Learning, Semiconductors or Genomics are facing the brunt of competition. They’ll envision to deliver high-end compute-intensive applications with high performance. To entice such industries, Cloud providers will start imparting HPC capabilities in their platform. We’ll also witness large scale automation in Cloud.7. Artificial IntelligenceFor 2019 AI/ML will come out of the research and development model to be widely implemented in organizations. Customer engagements, infrastructure optimization, and Glass-Box AI, will be in the forefront.7.1 AI to Revive Customer EngagementsBusinesses (startups or enterprise) will leverage AI/ML to enable a rich end-user experience. Per Adobe, enterprises using AI will more than double in 2019. Tech and non-tech companies, alike, will strive to offer personalized services leveraging Natural Language Processing. The focus will remain to create a cognitive customer persona to generate tangible business impacts.7.2 AI for Infrastructure OptimizationIn 2019, there will a spur in the development of AI embedded monitoring tools. This’ll help companies to create a nimble infrastructure to respond to the changing workload. With such AI-driven machines, they’ll aim to cut down the infrastructure latency, infuse robustness in applications, enhance performances, and amplify outputs.7.3 Glass-Box AI will be crucial in Retail, Finance, and HealthcareThis is where Explainable AI will play its role. Glass-Box AI will create key customers’ insights with underlying methods, errors or biases. In this way, retailers don’t necessarily follow every suggestion. They can sort out responses that fit rights in that present scenario. The bottom-line will be to avoid customer altercations and bring out fairness in the process.

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AI automation Autonomous Storage Cloud Interoperability Containers devops DRaaS Embedded AI FaaS Glass-Box AI

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