ACTS in Need: Automatic Configuration Tuning with Scalability Guarantees

TL;DR

This paper introduces ACTS, an automatic configuration tuning method with scalability guarantees, designed to optimize complex Big Data systems' performance and resource utilization efficiently.

Contribution

We propose and implement ACTS, a scalable automatic tuning framework that addresses multi-system interactions and diverse configurations in Big Data environments.

Findings

ACTS improves system performance significantly.

ACTS reduces resource usage and costs.

ACTS enables fairer benchmarking.

Abstract

To support the variety of Big Data use cases, many Big Data related systems expose a large number of user-specifiable configuration parameters. Highlighted in our experiments, a MySQL deployment with well-tuned configuration parameters achieves a peak throughput as 12 times much as one with the default setting. However, finding the best setting for the tens or hundreds of configuration parameters is mission impossible for ordinary users. Worse still, many Big Data applications require the support of multiple systems co-deployed in the same cluster. As these co-deployed systems can interact to affect the overall performance, they must be tuned together. Automatic configuration tuning with scalability guarantees (ACTS) is in need to help system users. Solutions to ACTS must scale to various systems, workloads, deployments, parameters and resource limits. Proposing and implementing an ACTS solution, we demonstrate that ACTS can benefit users not only in improving system performance and resource utilization, but also in saving costs and enabling fairer benchmarking.