How AI Solves the Kubernetes Complexity Conundrum
Kubernetes has certainly earned its reputation as a pivotal part in enterprise IT’s shift from yesterday’s monoliths to today’s more dynamic microservices, containers and multi-cloud infrastructures.
But the shift has been a double-edged sword.
On the one hand, Kubernetes is a major catalyst for accelerating enterprise digital transformation, providing new opportunities for building, deploying and managing applications and clouds. In a 2018 Cloud Native Computing Foundation (CNCF) survey of 5,000 enterprises, 40% of enterprises (5,000+ employees) said they were running Kubernetes in production and 58% of all respondents were using it in production. Kubernetes also greatly outpaced other container management tools in the CNCF study, being adopted by 83% of enterprises.
But on the other hand, Kubernetes has also introduced new layers of complexity. That means DevOps and IT put less time and resources toward driving new proactive innovations, and more into reactive problem solving and putting out fires in an environment that is more dynamic and unpredictable than ever.
Kubernetes offers enterprises a valuable new tool for kicking digital transformations and time-to-market timetables into a new gear. But to take full advantage of it, IT and DevOps need to drive a culture shift toward AI and automation; most other existing IT approaches simply do not adequately work or scale in this new world.
What Kubernetes Brings to the Table
Kubernetes’ native cluster and node health data help to paint a fuller picture of complex cloud environments. That data provides more precise answers and insights into multi-cloud environments, opening opportunities for faster innovation and increasing the time-to-market for new products, new services and new deployments.
But, in the enterprise Kubernetes environments I’ve seen, there are billions of interdependencies to account for. That volume of data gives IT more to work with than ever before, but it’s also an impossible amount of information for human beings to be able to make sense of in an instant. And that has knock-on effects, hurting everything from performance to user experience to production deployment speed.
But pairing together cloud native workloads orchestrated in Kubernetes with AI, and a culture of AIOps around it, provides enterprise IT with a way to unlock the full potential of what Kubernetes has to offer.
Pushing IT Culture in an AI Direction
I’ve long thought of DevOps as being 80% about culture. Not just in terms of changing IT culture to bridge the gaps between the Dev and Ops sides, but in making culture changes that properly integrate AI into IT workflows.
AI empowers IT to figure out problems faster. Why is this important to managing Kubernetes deployments? Because AI can make sense of more detailed data across cloud environments and applications than any human can ever do. When you’ve got an environment as dynamic and complex as Kubernetes, being able to sift through it as quickly and automatically as AI does is essential to getting the most out of what Kubernetes can do for an enterprise.
Consider what AI brings to the table:
- Detecting problems as they arise, and their root causes, in real-time.
- Automatically triggering processes for remediation to quickly and thoroughly resolve those problems.
- Automating interdependency mapping and full-stack monitoring to free up IT and DevOps to focus more on building and deploying new features into production.
- Empowering DevOps to further build automated tools into their pipeline, expediting the deployment of new builds and accelerating feedback cycles that enhance those builds.
A deterministic AI that has been optimized for the modern hybrid-cloud applications of the future and can crunch the data that humans cannot process anymore, and an AIOps culture change underpinning all this, is critical to making all this actually work.
The last thing IT and DevOps need in their lives is more complexity. Without AI, the environmental complexity that comes from Kubernetes dictates how IT and DevOps work, rather than the other way around. AI, ironically, puts the power back in the people’s hands.
Unlocking Kubernetes’ Full Potential with ‘Deterministic AI’
AI and Kubernetes complement each other. AI triggers fast, automatic, real-time insights into dynamic environments — mapping out interdependencies between applications, microservices and containers. That makes for faster innovation, better-informed decision making, successful Kubernetes deployments, optimized user experiences and stronger business outcomes. At the same time, bringing together Kubernetes cluster data, node health data and utilization metrics with a deterministic AI makes that AI even smarter, putting it in a stronger position to optimize Kubernetes workloads and accelerate cloud innovation.
On its own, Kubernetes can create more complexity for IT and DevOps teams already overrun by firefighting. But by integrating Kubernetes together with a deterministic AI and an AIOps culture around it, those teams can bypass this needless complexity and leverage Kubernetes deployments for driving new digital transformation and cloud innovation opportunities.