流处理系统性能优化：面向Kafka Streams的实验驱动配置调优
Performance Optimization in Stream Processing Systems: Experiment-Driven Configuration Tuning for Kafka Streams
AI总结：
本文提出了一种基于实验驱动的自动化配置优化方法，通过拉丁超立方采样、模拟退火和梯度爬升三种技术，提升Kafka Streams在云原生环境下的性能表现。
摘要：
在云原生部署中对流处理系统进行高效性能配置是一项具有挑战性且大多手动的任务。我们提出了一种实验驱动的方法，用于自动化配置优化，该方法结合了三个阶段：拉丁超立方采样用于初始探索，模拟退火用于引导随机搜索，以及梯度爬升用于局部优化。该工作流与云原生的Theodolite基准测试框架集成，使能够在Kubernetes上自动化实验编排，并提前终止表现不佳的配置。在针对Kafka Streams和基于Kubernetes的云测试平台的实验评估中，我们的方法识别出能够将吞吐量提升高达23%的配置。结果表明，拉丁超立方采样结合提前终止和模拟退火在导航配置空间中特别有效，而通过梯度爬升进行的额外微调带来的收益有限。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
