Domain-Knowledge Driven Configuration Tuning of Kubernetes Workloads

Abstract

Industrial software applications often require engineers to tune the software deployment platform. Configuration tuning identifies valuable configurations that minimize costs while ensuring the application performance. However, finding these configurations is difficult. The number of tunable configuration parameters in industrial applications is large, and engineers cannot assess different configurations in production because users may experience performance degradation. This work targets Kubernetes: A system for automating the deployment, scaling, and management of containerized applications. This paper proposes a domain-knowledge-driven solution for finding configurations of industrial applications in a practical time. The solution relies on a catalog of constraints encountered by domain experts in similar applications. Depending on the problem under analysis, engineers can customize the optimization problem by selecting the relevant constraints from the catalog for the application at hand. We rely on two alternative solvers to find an optimal configuration: The first is based on heuristics, while the second reuses existing optimization-based solutions. We implemented our solution within an industrial tool from a software company specializing in performance engineering and optimization. We compare our solution to a baseline, considering a set of predefined configuration values provided by engineers. Our results confirm the effectiveness of our solution: Compared to the baseline, the heuristic-based and optimization-based solutions provide significant average savings of 11591 USD/mo and 15874 USD/mo per Kubernetes, respectively, accounting for 24.03% and 32.90% of the costs, in practical time (0.71 seconds per extraction for the heuristic-based and 45.54 seconds per extraction for the optimization-based solution).