New ICPE 2025 paper on configuration debugging of scalable Kubernetes clusters
Ever wondered why Kubernetes deployments can be so slow? â¸ď¸
Kubernetes exposes ~1,600 configuration parameters across a complex deployment pipeline, making performance tuning a challenging task. Our work introduces Columbo, a reasoning framework that identifies deployment bottlenecks and recommends configuration changes to address them. Columbo reduces workload deployment time by 28% on average and up to 79%, showing the potential of systematic configuration tuning.
Title: Columbo: A Reasoning Framework for Kubernetesâ Configuration Space
Authors: Matthijs Jansen, Sacheendra Talluri, Krijn Doekemeijer, Nick Tehrany, Alexandru Iosup (VU Amsterdam), Animesh Trivedi (IBM Research)
Abstract: Resource managers such as Kubernetes are rapidly evolving to support low-latency and scalable computing paradigms such as serverless and granular computing. As a result, Kubernetes supports dozens of workload deployment models and exposes roughly 1,600 configuration parameters. Previous work has shown that parameter tuning can significantly improve Kubernetesâ performance, but identifying which parameters impact performance and should be tuned remains challenging. To help users optimize their Kubernetes deployments, we present Columbo, an offline reasoning framework to detect and resolve performance bottlenecks using configuration parameters. We study Kubernetes and define its workload deployment pipeline of 6 stages and 26 steps. To detect bottlenecks, Columbo uses an analytical model to predict the best-case deployment time of a workload per pipeline stage and compares it to empirical data from a novel benchmark suite. Columbo then uses a rule-based methodology to recommend parameter updates based on the detected bottleneck, deployed workload, and mapping of configurations to pipeline stages. We demonstrate that Columbo reduces workload deployment time across its benchmark suite by 28% on average and 79% at most. We report a total execution time decrease of 17% for data processing with Spark and up to 20% for serverless workflows with OpenWhisk. Columbo is open-source and available at https://github.com/atlarge-research/continuum/tree/columbo.
ACM: https://dl.acm.org/doi/10.1145/3676151.3719374