Efficiency is one of Kubernetes’ top benefits, yet companies adopting Kubernetes often experience high infrastructure costs and performance issues, with applications failing to match latency SLOs. Even for experienced Performance Engineers and SREs, sizing of resource requests and limits to ensure application SLOs can be a real challenge due to the complexity of Kubernetes resource management mechanisms.
In this talk at USENIX SREcon on 2021, October 14th, Stefano Doni (Akamas CTO) describes how AI techniques help optimize Kubernetes and match SLOs. The first part of the presentation Stefano covered the Kubernetes resource management concepts from a performance and reliability perspective. In the second part of the talk, the AI techniques are applied to a real-world cloud-native application to achieve the perfect balance among low costs and optimal application performance and reliability.