Seminario de Investigación: Machine Advice for GitOps-managed Kubernetes configuration optimisation

Josef Spillner

Talk title: Machine Advice for GitOps-managed Kubernetes configuration optimisation

Abstract: The profitability of cloud providers is often negatively affected by misconfiguration of application resource constraints, in particular in conjunction with autoscaling. In this research study, we check the feasibility of integrating ML on usage-dependent configurations into a GitOps workflow. The configuration scope encompasses resource constraints and autoscaling in Kubernetes (HPA, VPA, KEDA). The result will be a novel advisor service that tells GitOps engineers about monetary implications of detected misconfigurations.

Associated research questions are: Can existing ML components be integrated into GitOps workflows? Are they scalable enough to process metrics from a growing customer base? Do they identify the parts of the configuration that are the low-hanging fruits in the sense of saving most resource expenses after adopting few suggested changes?

Bio short: Dr.-Ing. habil. Josef Spillner is a senior lecturer / associate professor at Zurich University of Applied Sciences in Switzerland. His research activity focuses on Distributed Application Computing Paradigms. This involves distributed, federated and decentralised application designs, as well as novel cloud and post-cloud application architectures, in a spectrum ranging from basic research to applied innovation and industry transfer. Particular emphasis is on technological support for emerging digitalisation needs of industry and society, such as smart cities and mobility. His teaching schedule is focused on Big Data Computing, Cloud Native Computing and Serverless Computing, and Infrastructures for Data Science. He is a senior member of IEEE, member of ACM and national professional societies.

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