Dynatrace: Simplify Kubernetes Complexity with Advanced AIOps and Cloud Observability

Kubernetes accelerates the speed and reliability of software development, but complexity is a common problem. Learn how Dynatrace takes the complexity out of Kubernetes with AIOps-backed cloud observability.

As more and more organizations turn to containerizing applications, managing the tasks and processes that come with containers becomes critical. Kubernetes helps organizations better manage containerized workloads and services. In turn, it paves the way for fast, functional and reliable software development. However, at scale, these deployments can encounter a common obstacle: complexity. This is where AIOps comes in.

To combat the complexity of Kubernetes and take full advantage of the open source container orchestration platform, organizations need advanced AIOps that can intelligently manage the environment. Cloud-native observability and artificial intelligence (AI) can help organizations do just that with enhanced analytics and targeted insights.

At Dynatrace Perform 2022, Dynatrace Product Manager Florian Geigl and Senior Product Manager Matt Reider discuss the top DevOps challenges of Kubernetes complexity and explore how Dynatrace is streamlining operations. Additionally, they discuss the need for cloud-native observability with GitOps that provides continuous operational insights across the entire Kubernetes value stream.

Cloud-Native Observability Delivers Enhanced Kubernetes Insights

Cloud-native technologies allow organizations to build and run scalable applications in environments such as public, private, and hybrid clouds. With these environments, businesses can take advantage of increased flexibility and scalability.

There are many options for conducting cloud-native operations, such as containers, service meshes, microservices, immutable infrastructure, and declarative APIs. Therefore, says Reider, it’s not about the specific technologies used by companies.

Businesses can use any set of cloud-based platforms, tools, and solutions in a cloud-native approach. What matters more than the components is the ability to understand what’s going on in the operational stack anytime, anywhere. Dynatrace offers organizations robust, cloud-native observability that provides continuous insight into operations across the entire Kubernetes value stream – throughout planning, validations, testing, observation, and analysis .

While Kubernetes excels at rolling current compute states back to desired states, issues can arise when business conditions change. Kubernetes’ efforts to restore compute conditions can lead to process evictions, out-of-memory deletions, and workload throttling, which reduces overall performance. “That’s really the job of Kubernetes, which is to try to run the declared state in production – always trying to bring it back to that declared state,” says Reider.

Dynatrace’s cloud-native observability is key to monitoring and managing these performance issues as they arise.

“The desired state of our compute resources [is] suddenly out of step with the current state of these resources. So Kubernetes will try to bring that desired state back to its current state by… evicting less important jobs that affect our compute resources, or killing jobs that exceed our memory limit, or throttling jobs that exceed our memory limits. CPU,” says Reider. . “This cycle leads us to make changes. These changes can only be made by using Dynatrace to analyze what these workloads should look like and what the memory and CPU limits should be based on these new business terms. . »

Solve key Kubernetes computational issues

If observability is the starting point, enterprises also benefit from agile AIOps to manage Kubernetes. AIOps can help solve three key complexities of Kubernetes:

  • Requirements and Limits. Kubernetes makes it easy to set demands and limits. For example, a container request reserves 500 CPU mCores that users can access for specific operations and has a limit of one core, which limits the process if it needs more. This leaves room for flexibility. Processes can use more than 500 mCores until they reach the hard limit or the current node runs out of CPU. However, as environments evolve, processes can be throttled or squeezed out unexpectedly.
  • Waste of overprovisioned resources. Kubernetes deployments can also result in wasted overprovisioned resources, in part due to the “IKEA effect”. This speaks to the higher value that users place on services or processes that they build themselves. This results in higher demands that lead to significantly overprovisioned resources – collectively costing organizations up to $6 billion per year worldwide.
  • Secure resizing of resources. Another challenge organizations often face is under-provisioning resources to address over-provisioning issues. While the goal is to safely scale resources, Geigl points to the impact of the “butterfly effect,” which occurs when seemingly unrelated processes have a significant effect on overall performance. To avoid this, organizations need an AI solution that not only reveals a performance issue, but how that issue affects the entire system.

Managing Kubernetes Complexity with Davis Intelligence

So how does Dynatrace help manage Kubernetes intelligently?

First, the dashboard provides an overview of the entire Kubernetes workload, showing current properties, container usage, and existing Kubernetes pods. Then, the dashboard dynamically assesses the current Kubernetes workload sizing, which includes workloads that are both undercommitted and overcommitted.

Additionally, Dynatrace’s deterministic and causal-based AIOps engine, Davis, details front-end response time and throughput. From there, Davis identifies specific issues, such as response time degradation. Then, it ties these issues to specific business effects and metric anomalies.

More importantly, Davis identifies root causes so teams can act immediately. “Davis provides the safety net here,” says Geigl. “It alerts on the service that has been impacted due to under-engagement.”

For example, Davis can show users how undercommitting resources has slowed down a service. Moreover, the deterministic AI solution highlights how this slowdown of a single service had a huge impact on the entire system. This can lead to poor user experience. Davis is the only AI engine that connects the dots between departments and understands how small changes can have a big effect on complex systems.

Keeping Kubernetes Simple with Dynatrace

Kubernetes remains a core part of DevOps and GitOps functionality. But simply deploying Kubernetes is not enough. To get the most out of this container orchestration system, organizations need cloud-native observability backed by AIOps to automatically discover what’s happening in their development stack, identify key issues, and take action to improve the overall result.

To learn more about how Dynatrace simplifies Kubernetes cloud-native management with AIOps, check out the “Simplify Kubernetes Complexity with Advanced AIOps” session.

Sharon D. Cole