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Tag: IT operations

  • The CMDB Is Dead – Long Live the CMDB

    The CMDB Is Dead – Long Live the CMDB

     Many enterprise IT organizations have a configuration management database, or CMDB, in place. These systems are important for overall configuration management, governance and DevOps coordination. Yet, these legacy data repository systems can fall short in our dynamic IT environment. They require manual updating and can’t always support real-time decision-making. Furthermore, we have new requirements since the onset of widespread WFH workplaces. In this article, we talk about the past and current role of the CMDB, how it is evolving and how integrating it with other IT operations systems can modernize infrastructure monitoring and configuration management.

    History Lesson

    Remember the enormous fuss over Y2K? IT execs were concerned that the new century turnover would break their systems, which led to a secondary concern that they weren’t sure what was even on the network. The 2001 release of ITIL v2 best practice led to the notion of a CMDB, which would enable the tracking of every technology asset and its configurations and show the relationship between those assets. But getting all that data in one place proved to be a problem—and one that has persisted to this day.

    Is the CMDB Still Relevant?

    CMDBs have been around for a long time, and they still play a valuable role in the enterprise IT organization. In fact, a CMDB has never been more essential than now. The year 2020 has been chaotic for most IT organizations as employees quickly transitioned to working from home and are using a wide array of personal devices and networks, all of which need to be monitored, maintained and secured. As shadow IT persists and organizations move increasingly toward rapid deployment and release models that incur frequent, ongoing change, governance and control become critical.

    Contrary to what some may think, you can’t safely go faster without a level of control. This is a balance that DevOps practitioners must strike, and it entails a deep understanding of all elements within the IT environment and how they relate to one another. If the enterprise doesn’t have a CMDB (or something akin to it) to provide visibility into infrastructure components, configurations and relationships, the IT operations team is working at a disadvantage. Such lack of knowledge slows down developers—and nobody wants that. Even worse, without always updated, reliable infrastructure data, DevOps teams risk a catastrophic result with each software release.

    Modern CMDB Requirements

    Today’s CMDB is critical for incident management and must be supported by a mature, well-understood change enablement practice in multi-cloud and hybrid environments, yet legacy platforms aren’t designed to monitor systems residing outside of the corporate firewall. More importantly, modern applications are built on containers and ephemeral microservices that appear and disappear frequently. It is nearly impossible for IT staff to manually update the CMDB under such constraints, and the truth is, people weren’t typically doing this regular updating years ago. Today, the only viable way to maintain state and configuration data to support accurate, real-time decision-making is through automation.

    Most CMDBs on the market today lack automation; they still look and act much like a data repository. The appetite for heavy manual work is razor-thin, and IT organizations simply don’t have the people power to do it regardless. Simultaneously, the role of the IT operations professional has evolved dramatically—it is no longer as focused on installing, patching and rebooting systems. Rather, these individuals are now expected to manage applications and platforms in an end-to-end fashion, serving the business with their unique requirements spanning distinct business units and use cases. Therefore, the more we can use technology to reduce the load of traditional incident management tasks and enable operations to be a true partner with development, the better we can serve the business and end customers.

    So, What Now?

    Integrating modern and legacy tools is one practical solution for resolving these issues. Newer IT operations management (ITOM) technologies that incorporate artificial intelligence and machine learning (AIOps) to automate the discovery of assets and topology mapping can bring the CMDB into the modern age. An open ITOM framework for integrations can keep the CMDB always up-to-date, without manual intervention. This enables IT operations staff to focus on optimizing and improving infrastructure for reliability and performance, versus digging into one dashboard after another to make sense of the status quo.

    Understand the requirements and scope of your CMDB. Tracking the configurations of components that are not business-critical only expands on the effort and resources needed to successfully maintain the CMDB, creating waste in the process. Keep it simple and iteratively improve the CMDB over time. Most importantly, get started now. Even seemingly small, simple improvements can have a positive cascading effect down the road.

    IT environments will only get more complex—even though the end goal is simplicity, cost savings and efficiency. To keep pace, enterprise IT organizations will need to rethink the traditional CMDB. By integrating the CMDB with modern IT operations monitoring, management and automation systems, IT operations teams can finally have a reliable repository of data on all of the company’s on-premises assets and multi-cloud resources, while also gaining the ability to automate manual tasks that impair productivity and eventually remediate where appropriate with the right approach to AI and machine learning.

     

    This article was co-authored by Curt Thorin, a solutions consultant at OpsRamp. He has 20+ years’ experience in enterprise IT software and services. Curt can be reached online at LinkedIn.

  • VMware Expands Reach of IT Management Portfolio

    VMware Expands Reach of IT Management Portfolio

    VMware today announced a bevy of updates to its portfolio of IT management tools that is now at the heart of its overall approach to both DevOps and AIOps.

    VMware vRealize Operations 8.2 and VMware vRealize Operations Cloud, the on-premises and software-as-a-service (SaaS) editions of the same IT automation platform, provide deeper support for Kubernetes distributions, including the VMware Tanzu Kubernetes Grid, as well as tighter integration with application performance management (APM) platforms from AppDynamics, Datadog and Dynatrace.

    VMware is also providing tighter integration with its VMware vRealize Network Insight to bridge the divide between IT and network operations and tools to better monitor instances of VMware software running on the Amazon Web Services (AWS) public cloud.

    Enhancements to the capacity and cost management tools that VMware makes available as part of the platform include more visibility into daily virtual machine (VM) costs, enhanced metering capabilities and support for non-VMware vRealize Automation workloads.

    At the same time, VMware is making updates to VMware vRealize Automation and VMware vRealize Automation Cloud. The latest updates to the VMware automation platform add support for VMware Cloud Templates, a way to manage infrastructure as code that is compatible with configurations created using Terraform tools.

    Other enhancements include self-service provisioning of Kubernetes namespaces via the service catalog, including support for VMware vSphere with Kubernetes, as well as more granular role-based access controls and application programming interfaces through which policies for VMware NSX network virtualization software can be applied.

    Updates to VMware vRealize Log Insight and vRealize Log Insight Cloud add enhanced Kubernetes support, deeper integration with VMware Cloud on AWS and other usability enhancements.

    Finally, VMware is streamlining the onboarding process for VMware Skyline, a predictive analytics tool the company makes available as an extension of its operations management portfolio.

    Ken Lee, senior director of product marketing for VMware, said VMware vRealize Cloud Management portfolio is evolving in a way that makes it accessible to both traditional IT administrators and DevOps teams. Each function in the platform can be invoked via a graphical user interface or application programming interface (API), he said.

    At the same time, VMware is infusing machine learning algorithms across the portfolio to enable AIOps, eliminating the need for IT organizations to acquire a separate IT management platform to employ AIOps or embrace best DevOps practices, he said.

    While the number of IT organizations that employ virtual machine software from VMware numbers in the hundreds of thousands, the number of organizations employing its IT operations platform is still measured in the hundreds. The company clearly enjoys the inside track in terms of relationships with those customers. However, it’s clear that the number of IT organizations relying on tools other than those provided by VMware to manage their environments is significant.

    As IT organizations look to modernize those IT environments, however, there is clearly an opportunity for VMware to gain additional traction. The challenge is the race to embed AI and DevOps practices into IT operations platforms is already quite heated.

  • VMware’s Blue Medora Buy Advances AIOps

    VMware’s Blue Medora Buy Advances AIOps

    VMware has announced it will acquire Blue Medora, a provider of a service for integrating data collected by IT monitoring tools, to expand its efforts to advance the adoption of artificial intelligence to automate IT operations (AIOps).

    Roy Ritthaler, vice president of cloud management for VMware, said Blue Medora provides VMware with a software-as-a-service (SaaS) platform that is employed to inform VMware vRealize, an IT automation suite that is now being used by more than 400 customers.

    The data Blue Medora collects will also be employed within Project Magna, a research and development initiative that applies machine learning algorithms and other forms of artificial intelligence (AI) to automate IT operations, he said. Project Magna is based on a reinforcement learning engine that automates the tuning of multiple sub-components of the overall IT environment, resulting in what VMware calls “self-driving data centers.”

    Ritthaler said the goal is not to replace IT professionals as much as it is to eliminate rote tasks while making it easier to manage increasingly complex IT environments at scale. It then will become feasible for a smaller number of IT professionals to manage hybrid cloud computing environments, he noted.

    Blue Medora’s True Visibility Suite provides VMware with visibility not just into IT infrastructure but also all the way up the application stack.

    Ritthaler said it’s only a matter of time before most management frameworks are accessed as service, given all the data that needs to be collected and then analyzed to surface meaningful insights. It’s not clear how roles with IT organizations will evolve once those insights are surfaced regularly. Most enterprise IT organizations today rely on multiple specialists to optimize all the components that make up the IT environment. In theory, AIOps will make it possible for IT generalists to manage an IT environment without having to rely on so many specialists.

    AIOps undoubtedly will also have a profound impact on DevOps processes. As more IT operations become automated, the amount of time and effort spent managing IT infrastructure should decline.

    Interest in IT automation is on the rise in the wake of the COVID-19 pandemic. With many IT professionals working from home as much as possible, organizations are looking to automate rote IT processes to make them easier to manage remotely. In many cases, achieving that goal will require IT teams to rely more on tools that are accessed as a cloud service.

    Regardless of the path forward, it’s apparent the management of IT is about to be utterly transformed.

    It’s not that IT organizations will suddenly have a greater appreciation of IT automation. However, the pandemic has added more urgency to an IT transition that was already underway prior to the pandemic. The issue now is not so much whether to embrace IT automation as it is determining what can be automated for a consistent outcome across IT environments that frequently are made up of a diverse yet not especially well-integrated range of platforms.

  • The Secret to Winning at DevOps: Are You Up for the Challenge?

    The Secret to Winning at DevOps: Are You Up for the Challenge?

    The main idea behind DevOps is to enable companies to keep up with the increased software velocity and advancements in agile culture for a smoother end-to-end software delivery cycle.

    The main goal of DevOps is to accomplish integration and automation, which is why implementing this philosophy can be challenging. Not only do you need to replace older methods and practices, but you’ll also have to change the mindset of the developers and the operations team to ensure they work in sync.

    Several IT operations teams and software teams have adopted DevOps practices into their work culture for faster evolvement and improved innovations. The pipeline comprises continuous integration, testing, deployment, as well as monitoring application and system performance.

    Introducing new DevOps tools and changing the mindset to prepare teams for the fast-paced world remains crucial for a successful implementation.

    Despite the revolution that this philosophy brings to software development, several enterprises find themselves taking DevOps to an unproductive extreme. To remain productive, one of the aspects that teams need to work on is reducing rework, and the best way to ensure this is automation.

    Understand How DevOps Works

    Under a DevOps model, the development and operations teams are integrated into one to prevent them from getting siloed. This also improves collaboration and productivity.

    You see, it brings alive the possibility of both the teams thinking more alike, working together and sharing responsibilities. This is done by automating code testing and workflows, automating infrastructure and continuously measuring application performance.

    In some models, even the QA and security teams merge with the dev and ops. In cases where security becomes the primary focus, the name gets modified to DevSecOps.

    In essence, the focus of DevOps is on automating processes that were otherwise manual and slow. Teams use a technology stack and tooling that can operate and evolve applications more reliably and swiftly. As engineers will be able to accomplish tasks such as deploying code and provisioning infrastructure faster with reduced issues, teams can reach testing and maintenance stages quicker.

    Balance Between Standardization and Adaptability

    Software products are mostly developed from the customers’ perspective, and if not, from end-user experience. Often, developers work backward to identify customer needs and wants for ascertaining successful innovation.

    One of the biggest challenges when developing a software development life cycle (SDLC) strategy for organizations is to consider dynamic customer needs, strict compliance policies, unpredictable market trends and company goals. To ensure a consistent workflow, DevOps teams need to adopt standardization. In fact, workflows, technologies, processes, protocols and metrics need to be standardized before they can be automated.

    This is also the main reason why organizations adopting DevOps need to strike a balance between standardization and adaptability. One industry that has excelled at this has been the financial industry, which has been leading the charge when it comes to adopting the latest software delivery practices and technologies, including DevOps. Financial executives view DevOps as being a way to improve security and risk and governance strategies. 

    The result is that cloud-based financial services are now automating virtually all of their processes through their software delivering pipelines, which makes tasks such as scanning receipts, sending invoices, tracking expenses or making payments seamless as they sync to your account. This simpler process is beneficial for boosting developer productivity and resource utilization, and for enabling enterprise scale management. If anything automating processes is becoming necessary for achieving higher product quality, compliance and scalability. 

    As mentioned above, new DevOps tools need to be introduced to team members to keep up with the constantly evolving strategies and implementation. But to prevent the creation of silos between teams, mechanisms need to be developed to make the adoption of new technologies easier. 

    Achieving the delicate balance between standardization and automation will help eliminate communication gaps, performance bottlenecks and silos between the different teams.

    Benefits of Automation in the DevOps Landscape

    The following are the advantages of adopting a DevOps philosophy:

    • Code Development. Source control will keep everyone in the loop by managing, tracking and documenting any changes in code. This can be time-saving, and reduce the complexity of software projects.
    • Smoother Testing. Testing is a continuous process in DevOps frameworks that includes system communication, several test integrations, predicting and tracking issues, and integrating approved automated builds. In the absence of automation, executing and managing continuous testing could be a rather tedious job that might cause problems between developers and the operations team.
    • Better Visibility. The main job of the ops teams is to be aware of the code changes, ongoing problems and the impact of a specific code on the end goals. Plus, when they operate in a DevOps environment, they need to keep the developers in the loop as well.
      For example, if a system administrator from the ops team identifies code issues weeks after submission, it puts more pressure on developers. Now, the developers have two jobs: solve the problem in the previous code, and simultaneously work on making progress with the new code. This is what gets time-consuming, which can harm the company’s market standing. To remain ahead in the competition, innovation needs to be frequent and accurate.
      When there is a constant feedback loop between the dev, ops and QA, bottlenecks, and silos are eliminated, productivity is improved and customers are happier due to quicker innovation.
    • Continuous Integration and Delivery. Automation can help deliver appropriate software built, data, code changes and tests to target environments. An example is how web hosts for e-commerce businesses need to be updated frequently for robust deployments. Automated testing will help ensure that code is delivered properly and doesn’t introduce new bugs, thus creating a platform with a superior user experience.
      This, in turn, helps DevOps teams to introduce frequent software changes by deploying error-free codes and continuous testing.
    • Data Processing and Monitoring. The teams also need to measure and monitor the performance of their environment, which can be done by reading and analyzing system logs. But the existence of hundreds or thousands of servers in the environment can make it complicated.
      Automation will allow the teams to make sense of all the data and let them know how an application is performing and whether there are any bottlenecks.

    Getting the Balance Right

    Organizations need to realize that automation involves placing technologies around once-manual processes. The catch here is as the processes will become inherently repeatable, they need to be correct. Otherwise, organizations will only have flawed processes running faster, which will ultimately defeat the purpose of adopting DevOps in the first place.

    Companies need to understand adaptability as well. This can be done by mixing both the old as well as new systems. For this purpose, an inventory of all applications, data stores and development processes for promoting meta development processes that include design, development, testing, deployment, configuration management and so on can be made.

    In the end, the secret to winning DevOps is aligning DevOps tools with your organization’s goals – not following the footsteps of other organizations.

  • Fostering Better Collaboration Between ITOps and Security Teams Should Be Top Priority

    Fostering Better Collaboration Between ITOps and Security Teams Should Be Top Priority

    Last year, the average business lost $13 million to cybercrime, which is 12% more than in 2017 and 72% more than in 2014. When Tanium recently asked IT decision makers how they planned to reduce that risk in 2020, they reported making multi-million dollar investments across operations and security. Over the last two years, they increased operations budgets by 11% and allocated 18% more funding to security teams.

    However, despite massive budget increases, both operations and security teams continue to struggle. Only half of the teams that Tanium surveyed said they had full visibility into their environments’ vulnerabilities and risks (51%), and less than half said they had full visibility into all of the hardware and software connected to their networks.

    Businesses are investing more in tools than ever before, but the issue isn’t more investment — it’s the right investment. “More tools” aren’t bringing siloed IT teams closer together, and aren’t achieving the kind of total visibility and control of all endpoints needed for effective risk mitigation.

    Here are some ways to reconsider the crucial collaboration between IT operations and security teams.

    Find Areas to Align

    While just 27% of the enterprises we consulted on these issues would describe their IT teams as “embedded,” as many as 67% said that coordination among teams is extremely challenging. Aligning the security and operations teams is one way to overcome this challenge. Different teams using different tools and receiving different data sets have little room for compromise, but teams united by a common toolset are not only more likely to collaborate; they are also better resourced to mitigate risk and protect valuable data. As teams do away with the point tools and invest in coordinated, platform-based solutions, their ability to easily verify the quality of their data, trust their vulnerability scans and remediate threats at scale will significantly improve.

    Prioritize Visibility Across the Environment

    While most IT decision makers (80%) believed they could instantly act on the results of their own vulnerability scans, fewer than half (49%) thought they had full visibility into all the hardware and software assets. But without full visibility, the vulnerability scans are merely good enough, and unable to account for areas managed by other teams. That false confidence is even worse in IT departments that have a rift between security and operations.

    If businesses fail to gain a view of the entire environment, it could take them two weeks longer to patch IT vulnerabilities (37 business days versus 27.8 business days for teams with a healthy relationship). By implementing an end-to-end view of the entire environment, both teams will be able to gain the visibility of the computing devices they need to protect organizational data.

    Reduce Complexity Between Teams

    More tools lead to a more complex environment. Tanium’s research showed that IT operations and security purchased an average of five new tools each within the last two years, and large organizations reported using as many as 40 to 50 point solutions. It’s no wonder why this happens: As new problems emerge, each team seeks a new solution based on that individual problem, ultimately creating an environment too difficult to manage. To keep an organization nimble and responsive, ITOps and security must work together to select solutions that are capable of solving the range of problems that both teams face, and agree on what data is most important.

    Create a Culture of Improved IT Hygiene

    As many as 80% of the organizations we spoke to said that maintaining IT hygiene was the most challenging task for IT operations and security teams. Once the entire department begins to prioritize the fundamentals, response times will improve and prevention and detection methods will sharpen.

    Conclusion

    Businesses that do not take these steps are vulnerable to disruption. It is essential that IT operations and security teams unite around a common set of actionable data and are empowered to ask questions about the state of every endpoint across the enterprise, retrieve data on their current and historical state, and execute change as necessary. These teams must come together from day one to maintain performance and security within their organizations — or continue to face the threat of cyberattacks, outages and other business-crippling disruptions.

  • It’s Time to Take a Customer-First Approach to AIOps

    It’s Time to Take a Customer-First Approach to AIOps

    Artificial intelligence for IT operations (AIOps) has several use cases that IT operations managers can’t deny: reduce alert noise with statistically significant outcomes (up to 80%), correlate alerts and events to uncover the critical business issues immediately, analyze data across environments to find root causes and resolve routine issues (like patching) automatically. Gartner predicts that large enterprise use of AIOps tools to monitor applications and infrastructure will rise from 5% in 2018 to 30% in 2023.

    I’ve been working in the AIOps field for a few years now, and I’ve learned that many organizations, even with relatively modern IT operations management (ITOM) software, are behind the curve. Vendors keep releasing new algorithms, yet the original ones are barely being used or worse, still sitting on the shelf in the customer’s environment. This is because it can be difficult to understand when and how to use artificial intelligence (AI) tools in the daily workflow, and because IT professionals may not inherently trust (or even want) machine-driven recommendations and actions. 

    AI and machine learning technologies are upending traditional assumptions and conventions in many industries – and IT is no exception. In the land grab to bring to market the latest sensational AIOps capabilities, vendors have unwittingly deprioritized customer adoption. This has resulted in a chasm: The technology is getting ahead of the customer’s willingness and ability to implement these solutions in their production environment. 

    The feature-first perspective in the software industry is nothing new, yet for AIOps to reach its full potential, it’s time to take a customer-first perspective.

    Let’s walk through a few ideas to minimize barriers to adoption and speed up time to value for AIOps:

    Start on Adoption During Build

    Machine learning developers and product managers need to understand potential customer barriers during the design and test phase and put themselves in the user’s shoes. How will an individual interact with the system and what level of transparency is appropriate to ensure a frictionless experience? This is a best practice for any software development team, but given the mystique of AI software, it’s even more important. Document expectations for the user experience by giving users early access to the software, such as a beta program, where they can freely deliver feedback on UI, workflows and outcomes of AIOps features.

    Connect the Dots with Visualizations

    A significant barrier for any machine learning application is that users may not fully understand the use cases nor trust what the algorithms are doing. Ensure that software-driven actions and recommendations are transparent at the moment and location they occur in the application. It should be easy for users to see the cause and effect of machine-driven actions as well as how the AI reached a conclusion.

    The concept of “explainable AI” has become popular in the last year and vendors can help by introducing visualizations into their tools.  For instance, you could have graphs showing the progression of a model being trained or the learned sequences in alert correlations. In-app simulation tools can help a customer see the impact of an AI-delivered recommendation without actually making the change.

    Amp Up Training

    Create internal-facing white papers or videos to demonstrate the workings behind algorithms to your technical staff and tools especially designed for users. The best way to learn is by doing, so designing visual cues and tutorials within the application will be most effective. Ensure that you have dedicated AIOps subject matter experts who can guide customers through implementations and be on call for advice as needed. Common questions include, when should the AI be allowed to automate an action or decision and how can you override an automated decision? It’s not a bad idea to involve machine learning developers in creating training sessions and guides. Finally, the age-old concept of nurturing power users within the customer organization can go a long way.

    Diffuse Concerns Over Jobs

    Nobody wants to use a new technology which might eventually replace them. For the most part, AIOps is not going to jettison IT operations staff. Instead, AIOps will empower operations and support teams to handle increasing alert volume and complexity with existing staff, while ensuring that expensive engineers such as SREs can focus on higher-level data performance analysis and optimization initiatives.

    Design Algorithms for Flexibility

    Machine learning algorithms break if they cannot respond to the dynamic nature of modern workloads. For instance, if you design the algorithm based on rules dictating actions between specific applications and infrastructure components, what happens when those technologies disappear or are replaced, as they often do in the cloud? Now you’ve got gaps that could result in a catastrophic system failure and an AIOps tool that requires heavy maintenance. Algorithms should be responsive to environmental changes – such as incorporating neural net technology that can adapt to the shifting sands of the IT landscape.

    A survey conducted in April by OpsRamp found that nearly 70% of IT operations leaders plan to invest in AIOps to improve incident diagnosis, troubleshooting and resolution. By all accounts, the year 2020 has raised the bar for IT expectations around resiliency and user experience. AIOps promises to be a pivotal strategy for IT operations success, but it all depends upon user satisfaction and adoption of the technology. Vendors that incorporate customer adoption and value tactics into their development processes will be instrumental in pushing this market forward in a positive way.

  • Report: Software Delivery Slowed During Pandemic

    Report: Software Delivery Slowed During Pandemic

    A survey of more than 500 IT and development professionals conducted by Atlassian finds two-thirds of respondents have slowed down the frequency of software delivery since the advent of the COVID-19 pandemic.

    Patrick Hill, head of reliability process at Atlassian, said while developers may be creating more code than ever while they work from home, IT operations teams working from home are struggling to keep pace.

    In fact, the survey finds more than half of respondents (51%) reported incident response times have been slower since they began working remotely. At the same time, there’s been a 73% increase in demand for the digital services being provided, the survey finds.

    It’s too early to say to what degree slowing down the application delivery process might be a byproduct of the “new normal.” It’s more likely that IT operations teams need more time to re-engineer processes to return to the software release levels typically achieved prior to the pandemic, said Hill.

    In many cases, IT teams may also need more time to shift toward cloud-based frameworks for managing application release cycles that can be accessed from anywhere. Hill said Atlassian has seen a significant increase in the use of its IT management platforms since March. New signups have increased by approximately 125%, he noted.

    It’s also unclear to what degree an application release backlog may be building inside organizations. While developers may be launching more projects, Hill said many of those projects may be only to address bug fixes. As such, it may not take that long for IT operations teams to recover from the disruption caused by the pandemic.

    Nevertheless, Hill said most organizations are still coming to terms with the human impact of the pandemic. Many organizations, for example, might not decide to bring IT teams back into the office at all. In fact, there may not be any office to return to—many organizations might conclude there is no need, especially if productivity levels have not declined substantially. No doubt many organizations consequently will be closely monitoring application release cycles in the weeks and months ahead to determine to what impact working from home is having on IT productivity.

    Of course, not every member of the IT team is cut out to work from home. While many IT professionals may be introverts by nature, there are also a great many distractions at home that adversely impact productivity. Conversely, some IT professionals might even discover they have begun to work longer hours.

    In the meantime, Hill said IT managers would be well-advised to focus more on outcomes than metrics. The amount of code being released in a given week is not likely to be as important as making sure the right code is being released at the right time. With more demand than ever for application services, the greater the need there is for DevOps teams to focus their efforts on the components of an application that make the most critical difference to the business.

  • DevOps and AIOps: Better Together

    DevOps and AIOps: Better Together

    DevOps is an attempt to scale technology with humans but AIOps is the ultimate answer.

    In today’s world, where so many essential business tasks have become digitized, IT teams must deal with constant change while ensuring zero downtime.

    The irony is that, although IT has become business-critical, the productivity and agility of the people building and supporting the customer experiences has plummeted. This has serious consequences for businesses. When your apps and services malfunction, you lose customers and revenue drops. When IT teams focus mostly on fixing emergencies, agility suffers and innovation shrinks. You need seamless operations in order to grow, compete and thrive.

    Historically, IT teams grew along with technology, beginning with the mainframe through distributed computing. But, the advent of virtual computing led to a world of microservices and ephemeral logic. Now companies simply generate too much data for humans to monitor and understand manually and with legacy tools.

    This opened the floodgates for an AI-led operations evolution, known as Artificial Intelligence for IT Operations (AIOps). By streamlining and automating IT monitoring, AIOps has helped IT Ops teams regain control over IT environments, quickly detect and repair problems, and prevent outages.

    AIOps

    DevOps: Back to the Future?

    However,  there’s recently been a shift in technology operations to once again attempt to scale technology with humans. The DevOps movement has placed the ownership, support and success of their services on the developers that write the code. This has decentralized technology operations teams that primarily operated with little context and unclear escalation paths, creating several smaller teams of DevOps engineers. 

    DevOps teams work together, each on their own microservice, toward the company’s desired customer experience and business goals. Depending on the operations model, there can also be a dedicated site reliability engineering (SRE) team or individuals on the DevOps team with the primary SRE role of monitoring observability data to discover potential issues. In this scenario, SREs feed the insights they uncover back into the development cycle to correct and strengthen the reliability and scalability of the DevOps teams’ services.

    AIOps

    But, as incidents occur in real-time, the challenge remains with SREs and DevOps teams to achieve insights and awareness across their applications, infrastructure and, ultimately, business services. To understand where incidents are occurring and what impact they’re having on the services and customers, it’s necessary to surface important events from the noise, understand the relationships between alerts and obtain the context needed to engage the right teams and people.

    This leads to another challenge: Getting the right people to respond and resolve incidents before there’s any business impact. Each DevOps team has its own responsibilities and tools. Often, DevOps teams don’t communicate except through their APIs. When incidents occur that need immediate attention from multiple people from multiple geographically-dispersed teams, how do you contact them and get them to collaborate, considering they have complex and different on-call schedules and escalation processes?

    As companies continue to transform with a customer experience focus and a digital-first mentality, they realize the seriousness of these challenges, the magnitude of their monitoring data and its negative effect on their business. Part of transforming their business is transforming their technology operations: Enter AIOps with the light to shine on DevOps processes, leading to a manageable, efficient and profitable future. 

    What AIOps Brings to DevOps Teams

    AIOps provides a unique solution to address operational challenges and cover every aspect of your service assurance strategy and business.

    Bottom line: You need to free people to focus on mission-critical tasks and empower them to build better services for better customer experiences—not scale them along with technology in an attempt to operate it.

    AIOps allows you to scale by integrating with the tools and infrastructure you’ve invested in over the years and by adding critical layers of AIOps (intelligence) throughout. By applying patented AI and machine learning algorithms to your observability and monitoring data, AIOps learns your environment’s normal behaviors and generates alerts accordingly. This must be done at the edge, close to where digital services produce vast amounts of data.

    Based on the anomalies generated both locally and environmentally, AIOps will then surface and correlate important alerts from all sources into actionable, contextual insights. A true, comprehensive AIOps solution also identifies root causes and impacts, and prescribes potential solutions based on previous resolution steps and feedback. This all occurs in a virtual workspace, where team members can collaborate, visualize and provide feedback. 

    AIOps notifies and empowers the right people to take the right action. It simplifies and understands complex team structures, engagement methods, on-call schedules and escalation paths, ensuring the right people are engaged and collaborate, even when they’re located around the globe.

    When a situation or incident is surfaced, it is sent to users in real-time. Based on the insights derived from the underlying data and machine learning applied, AIOps knows the exact teams and people that need to take action, and the ones that need to be informed. These users have the context they need to respond, share a consistent view and stay in sync throughout the lifecycle of the incident. Once the incident is resolved, a streamlined post-mortem speeds up future response, by using similar incidents and predictive analysis to spot repetitive and future problems.

    AIOps: A Key Ingredient for Effective DevOps

    The only way to scale with the technology being created in today’s and tomorrow’s world by DevOps teams is with AI. It frees humans to focus on creating and improving the customer experience that drives maximum profitability for your business.

  • IBM Launches Ambitious AIOps Initiative

    IBM Launches Ambitious AIOps Initiative

    IBM today announced it will apply a range of artificial intelligence (AI) technologies to automate the management of IT operations and modernize applications, also known as AIOps.

    Announced at IBM’s Think Digital conference, Watson AIOps leverages algorithms to continuously optimize IT operations while Accelerator for Application Modernization with AI is a suite of tools that leverage continuous learning and interpretable AI models to help organizations stay current as modern of technology and platforms evolve.

    Made available as part of a Cloud Modernization Service engagement, the Application Modernization with AI tools span a range of tasks from applying AI to more accurately identifying software components that should be deployed as containers or converted into microservices to applying symbolic reasoning to dynamically define modernization steps that are surfaced via a natural language processing (NLP) engine.

    Watson AIOps, meanwhile, applies machine and deep learning algorithms to time series data, semi-structured logs, structured data and unstructured data spanning both IT incidents and human conversations to track the timeline of an issue. It then uses that data to correlate root causes, create an explainable diagnosis and generate a recommendation for the best course of action. Built on top of an instance of the OpenShift platform that IBM gained via its acquisition of Red Hat, the Watson AIOps also leverages semantic search techniques that can relate the current incident to past incidents to provide additional context.

    Rob Thomas, senior vice president for IBM Cloud and Data Platform, said Watson AIOps will be made accessible via Slack and comes integrated with Box cloud services. It also is integrated with IT monitoring solutions from vendors including Mattermost and ServiceNow. IBM is applying multiple AI technologies developed by IBM Research for various Watson services provided by IBM, including a Watson OpenScale initiative to make AI explainable to create Watson AIOps, he said.

    That explainability capability is especially critical when it comes to automating IT operations. In general, the main source of resistance to AIOps is a lack of transparency into how algorithms are determining how any given task should be automated. IT teams are concerned not only with whether they can roll back automation for a specific task but also about becoming locked into a specific AIOps platform.

    It is too early to say to what degree IT organizations will embrace AIOps. There is no doubt that in the wake of the COVID-19 pandemic there will be more pressure than ever to automate, especially among organizations that have had to downsize their IT staffs. However, IT automation manifests itself in many forms, not all of which employ machine and deep learning algorithms. At the same time, AI is being infused into every existing IT management tool, so it’s not clear to what degree a dedicated AIOps platform will be required.

    No one expects AI to eliminate the need for IT staff anytime soon. Algorithms, at best, can only augment IT professionals. The challenge now is defining precisely where that human-to-machine interface should optimally be applied.

  • DevOps Practices: How to Make Valuable Changes to Your Teams

    DevOps Practices: How to Make Valuable Changes to Your Teams

    DevOps has become a buzzword within the software development industry, promising rapid turnaround times for requested changes. As for developers, DevOps promises to increase productivity while reducing the risk of production failures. The book “The Unicorn Project” by Gene Kim describes this process for developers as The Five Ideals.

    In this article, we’ll focus on only two of them:

    • The Third Ideal — Improvement of daily work
    • The Fourth Ideal — Psychological safety

    Psychological safety and improvement of daily work make it easier for developers to create changes for their teams, and the work to implement these ideals is not difficult. From automated testing to production monitoring, there are many great things that even junior developers can help bring to their teams that will help their entire team make safer changes to their code.

    So, what are these easy changes that can be made? Below is a list of tasks that can be done to improve the projects you are assigned to, with most taking less than four hours to complete.

    1. Set up a continuous integration server for your project, such as Jenkins or Team City. This will allow for automated jobs to be run for your code.
    2. Create jobs to run testing for pull requests as well as testing jobs to run overnight. This will show the current state of your project’s tests on a nightly basis and will allow for the number of failing tests to be monitored over time.
    3. Add a code coverage tool to your automated tests. This will show what is being tested in your code and what is not being tested, creating opportunities to help shore up your teams testing suite as well as confirm that your changes will be covered by existing tests.
    4. Create an automated deployment job on your CI server. This will convert the manual deployment process in a one click job, allowing the code to be deployed on command.
    5. Modify your project to extract metrics of the project, allowing your code to easily be monitored for faults in production.

    The tasks highlighted above will quickly add value to your team while helping protect you for making dangerous changes to your team’s code base. I recently changed teams and have moved to a new web project. The project runs fine on my local, but it is hard to track what is affected by my changes. The team already has a CI server to build their code, but there is no point in setting up any testing jobs as the project has fewer tests than I have fingers.

    Instead, I created a monitoring tool that captures all requests to the website and records their results. With the tooling in place, my team can now see how long it takes for each part of the website to load, as well as how many errors are being produced. Now, when I make any changes, I can check these metrics to see if my change is causing issues to the site, increasing my confidence in my code.

    If you are interested in learning more about DevOps and how to take advantage of it, here are a few good reading resources to look into:

    • “The Unicorn Project,” by Gene Kim.
    • “The Phoenix Project,” by Gene Kim, Kevin Behr and George Spafford.
    • “The DevOps Handbook: How to Create World-Class Agility, Reliability, and Security in Technology Organizations,” by Gene Kim, Patrick Debois, Jez Humble and John Willis.