Multi-Objectivizing Software Configuration Tuning (for a single performance concern)

TL;DR

This paper introduces a meta multi-objectivization model (MMO) for software configuration tuning that uses an auxiliary objective to prevent local optima trapping, significantly improving efficiency and effectiveness over traditional single-objective methods.

Contribution

The paper proposes a novel MMO model that leverages an auxiliary performance objective to enhance search robustness in expensive, rugged configuration landscapes.

Findings

Up to 42% improvement in overcoming local optima

Uses as low as 24% of measurements compared to state-of-the-art methods

Effective across diverse real-world software systems

Abstract

Automatically tuning software configuration for optimizing a single performance attribute (e.g., minimizing latency) is not trivial, due to the nature of the configuration systems (e.g., complex landscape and expensive measurement). To deal with the problem, existing work has been focusing on developing various effective optimizers. However, a prominent issue that all these optimizers need to take care of is how to avoid the search being trapped in local optima -- a hard nut to crack for software configuration tuning due to its rugged and sparse landscape, and neighboring configurations tending to behave very differently. Overcoming such in an expensive measurement setting is even more challenging. In this paper, we take a different perspective to tackle this issue. Instead of focusing on improving the optimizer, we work on the level of optimization model. We do this by proposing a meta multi-objectivization model (MMO) that considers an auxiliary performance objective (e.g., throughput in addition to latency). What makes this model unique is that we do not optimize the auxiliary performance objective, but rather use it to make similarly-performing while different configurations less comparable (i.e. Pareto nondominated to each other), thus preventing the search from being trapped in local optima. Experiments on eight real-world software systems/environments with diverse performance attributes reveal that our MMO model is statistically more effective than state-of-the-art single-objective counterparts in overcoming local optima (up to 42% gain), while using as low as 24% of their measurements to achieve the same (or better) performance result.