BestConfig: Tapping the Performance Potential of Systems via Automatic Configuration Tuning

TL;DR

BestConfig is an automated system that efficiently tunes system configurations to significantly enhance performance across various systems and workloads, reducing manual effort and expertise required.

Contribution

It introduces a novel architecture with divide-and-diverge sampling and recursive bound-and-search algorithms for automatic configuration tuning.

Findings

Improves Tomcat throughput by 75%

Enhances Cassandra throughput by 63%

Reduces Hive join job runtime by 50%

Abstract

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 BestConfig, 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.