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arXiv · 2603.04027

Performance Optimization in Stream Processing Systems: Experiment-Driven Configuration Tuning for Kafka Streams

David Chen·Sören Henning·Kassiano Matteussi·Rick Rabiser
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Abstract

Configuring stream processing systems for efficient performance, especially in cloud-native deployments, is a challenging and largely manual task. We present an experiment-driven approach for automated configuration optimization that combines three phases: Latin Hypercube Sampling for initial exploration, Simulated Annealing for guided stochastic search, and Hill Climbing for local refinement. The workflow is integrated with the cloud-native Theodolite benchmarking framework, enabling automated experiment orchestration on Kubernetes and early termination of underperforming configurations. In an experimental evaluation with Kafka Streams and a Kubernetes-based cloud testbed, our approach identifies configurations that improve throughput by up to 23% over the default. The results indicate that Latin Hypercube Sampling with early termination and Simulated Annealing are particularly effective in navigating the configuration space, whereas additional fine-tuning via Hill Climbing yields limited benefits.

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David Chen, Sören Henning, Kassiano Matteussi, Rick Rabiser. 2026-03-04. Performance Optimization in Stream Processing Systems: Experiment-Driven Configuration Tuning for Kafka Streams. https://doi.org/10.1145/3777911.3800636

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