BestConfig:通过自动配置调优挖掘系统的性能潜力

Yuqing Zhu, Jianxun Liu, Mengying Guo, Yungang Bao, Wenlong Ma, Zhuoyue Liu, Kunpeng Song, Y. Yang
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引用次数: 165

摘要

越来越多的配置参数被提供给系统用户。但是,许多用户在不同的工作负载中使用一个配置设置,从而没有充分利用系统的性能潜力。良好的配置设置可以极大地提高部署系统在某些工作负载下的性能。但是,由于有数十或数百个参数,决定哪种配置设置可以带来最佳性能成为一项代价高昂的任务。虽然这样的任务需要系统和应用程序方面的专业知识,但用户通常缺乏这样的专业知识。为了帮助用户挖掘系统的性能潜力,我们提供了Best Config,这是一个在给定应用程序工作负载下的已部署系统的资源限制内自动查找最佳配置设置的系统。BestConfig设计了一个可扩展的体系结构,可以自动对一般系统进行配置调优。为了在有限的资源范围内优化系统配置,我们提出了分散采样方法和递归定界搜索算法。仅通过配置调整,BestConfig就可以使Tomcat的吞吐量提高75%,Cassandra的吞吐量提高63%,MySQL的吞吐量提高430%,Hive join job的运行时间减少约50%,Spark join job的运行时间减少约80%。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
BestConfig: tapping the performance potential of systems via automatic configuration tuning
An ever increasing number of configuration parameters are provided to system users. But many users have used one configuration setting across different workloads, leaving untapped the performance potential of systems. A good configuration setting can greatly improve the performance of a deployed system under certain workloads. But with tens or hundreds of parameters, it becomes a highly costly task to decide which configuration setting leads to the best performance. While such task requires the strong expertise in both the system and the application, users commonly lack such expertise. To help users tap the performance potential of systems, we present Best Config, a system for automatically finding a best configuration setting within a resource limit for a deployed system under a given application workload. BestConfig is designed with an extensible architecture to automate the configuration tuning for general systems. To tune system configurations within a resource limit, we propose the divide-and-diverge sampling method and the recursive bound-and-search algorithm. BestConfig can improve the throughput of Tomcat by 75%, that of Cassandra by 63%, that of MySQL by 430%, and reduce the running time of Hive join job by about 50% and that of Spark join job by about 80%, solely by configuration adjustment.
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