Autotuning Configurations in Distributed Systems for Performance Improvements Using Evolutionary Strategies

Distributed systems usually have many configurable parameters such as those included in common configuration files. Performance of distributed systems is partially dependent on these system configurations. While operators may choose default settings or manually tune parameters based on their experience and intuition, the resulted settings may not be the optimal one for specific services running on the distributed system. In this paper, we formulate the problem of autotuning configurations as a black-box optimization problem. This problem becomes quite challenging since the joint parameter search space is huge and also no explicit relationship between performance and configurations exists. We propose to use a well known evolutionary algorithm called Covariance Matrix Adaptation (CMA) to automatically tune system parameters. We compare CMA algorithm to another existing techniques called Smart Hill Climbing (SHC) and demonstrate that CMA algorithm outperforms SHC algorithm both on synthetic data and in a real system.