AutoConfig: Automatic Configuration Tuning for Distributed Message Systems

Abstract

Distributed message systems (DMSs) serve as the communication backbone for many real-time streaming data processing applications. To support the vast diversity of such applications, DMSs provide a large number of parameters to configure. However, It overwhelms for most users to configure these parameters well for better performance. Although many automatic configuration approaches have been proposed to address this issue, critical challenges still remain: 1) to train a better and robust performance prediction model using a limited number of samples, and 2) to search for a high-dimensional parameter space efficiently within a time constraint. In this paper, we propose AutoConfig – an automatic configuration system that can optimize producer-side throughput on DMSs. AutoConfig constructs a novel comparison-based model (CBM) that is more robust that the prediction-based model (PBM) used by previous learning-based approaches. Furthermore, AutoConfig uses a weighted Latin hypercube sampling (wLHS) approach to select a set of samples that can provide a better coverage over the high-dimensional parameter space. wLHS allows AutoConfig to search for more promising configurations using the trained CBM. We have implemented AutoConfig on the Kafka platform, and evaluated it using eight different testing scenarios deployed on a public cloud. Experimental results show that our CBM can obtain better results than that of PBM under the same random forests based model. Furthermore, AutoConfig outperforms default configurations by 215.40% on average, and five state-of-the-art configuration algorithms by 7.21%-64.56%.