Kubernetes Observability Challenges: The Need for an AI-Driven Solution
Kubernetes provides abstraction and simplicity with a declarative model to program complex deployments. However, this abstraction and simplicity create complexity when debugging microservices in this abstract layer. The following four vectors make it challenging to troubleshoot microservices.
- The first vector is the Kubernetes microservices architecture, where tens to hundreds of microservices communicate. Debugging such a componentized application is challenging and requires specialized tools.
- The second vector is the distributed infrastructure spread across heterogeneous on-premises and cloud environments.
- The third vector of complexity is the dynamic nature of Kubernetes infrastructure. The platform spins up required resources and provides an ephemeral infrastructure environment to scale the application based on demand.
- Lastly, in such a distributed environment, Kubernetes deployments need fine-grained security and an observability model with defense-in-depth to keep them secure. While modern security controls effectively protect your workloads, they can have unintended consequences by preventing applications from running smoothly and creating an additional layer of complexity when debugging applications.
Today, DevOps and SRE teams must stitch together an enormous amount of data from multiple, disparate systems that monitor infrastructure and services layers in order to troubleshoot Kubernetes microservices issues. Not only is it overwhelming to stitch this data, but troubleshooting using Continue reading


