The Kubernetes performance tuning dilemma: How to solve it with AI

There is a “dark side” to Kubernetes that makes it difficult to ensure the desired performance and resilience of cloud-native applications, while also keeping their costs under control. Indeed, the combined effect of Kubernetes resource management mechanisms and application runtime heuristics may cause serious performance and resilience risks.

There are also significant potential improvements, both in terms of performance and efficiency, that can be achieved by properly tuning Kubernetes and application runtime (e.g. JVM, Golang) configuration settings.

In this webinar, we illustrate how the Akamas AI-powered optimizations platform addresses these challenges by making it possible to set the optimization goals (e.g. cost reduction) and constraints (e.g. performance SLOs) and get recommendations on how to adjust configuration settings dynamically under varying workloads.