ClassyTune: A Performance Auto-Tuner for Systems in the Cloud

TL;DR

ClassyTune is a machine learning-based auto-tuning tool that significantly improves cloud system performance and resource efficiency by effectively handling high-dimensional configurations and sample scarcity.

Contribution

It introduces a classification-based approach for auto-tuning cloud systems, addressing challenges of high dimensionality and limited training data.

Findings

Achieves up to seven times performance improvement.

Outperforms expert tuning and existing auto-tuning methods.

Reduces 33% computing resources for a cloud service.

Abstract

Performance tuning can improve the system performance and thus enable the reduction of cloud computing resources needed to support an application. Due to the ever increasing number of parameters and complexity of systems, there is a necessity to automate performance tuning for the complicated systems in the cloud. The state-of-the-art tuning methods are adopting either the experience-driven tuning approach or the data-driven one. Data-driven tuning is attracting increasing attentions, as it has wider applicability. But existing data-driven methods cannot fully address the challenges of sample scarcity and high dimensionality simultaneously. We present ClassyTune, a data-driven automatic configuration tuning tool for cloud systems. ClassyTune exploits the machine learning model of classification for auto-tuning. This exploitation enables the induction of more training samples without increasing the input dimension. Experiments on seven popular systems in the cloud show that ClassyTune can effectively tune system performance to seven times higher for high-dimensional configuration space, outperforming expert tuning and the state-of-the-art auto-tuning solutions. We also describe a use case in which performance tuning enables the reduction of 33% computing resources needed to run an online stateless service.