An Uncertainty-Aware Approach to Optimal Configuration of Stream Processing Systems

TL;DR

This paper introduces BO4CO, a Bayesian optimization method using Gaussian Processes to efficiently find optimal configurations for stream processing systems with limited experiments, significantly improving performance.

Contribution

The paper presents a novel auto-tuning algorithm, BO4CO, that effectively utilizes Bayesian optimization for configuration tuning in SPS, outperforming existing methods.

Findings

BO4CO locates optimal configurations within limited experimental budgets.

Performance improvements of at least an order of magnitude over existing algorithms.

Validation on Apache Storm demonstrates effectiveness.

Abstract

Finding optimal configurations for Stream Processing Systems (SPS) is a challenging problem due to the large number of parameters that can influence their performance and the lack of analytical models to anticipate the effect of a change. To tackle this issue, we consider tuning methods where an experimenter is given a limited budget of experiments and needs to carefully allocate this budget to find optimal configurations. We propose in this setting Bayesian Optimization for Configuration Optimization (BO4CO), an auto-tuning algorithm that leverages Gaussian Processes (GPs) to iteratively capture posterior distributions of the configuration spaces and sequentially drive the experimentation. Validation based on Apache Storm demonstrates that our approach locates optimal configurations within a limited experimental budget, with an improvement of SPS performance typically of at least an order of magnitude compared to existing configuration algorithms.