Meta Multi-Objectivization for Software Configuration Tuning

Core Concepts

Proposing a Meta Multi-Objectivization (MMO) model to optimize software configuration tuning by balancing target and auxiliary performance objectives.

Abstract

Software configuration tuning is crucial for optimizing performance objectives. The MMO model introduces two meta-objectives to prevent local optima traps while focusing on the primary objective. Normalization methods help balance the weight parameter sensitivity in the MMO model, improving efficiency and effectiveness.

Stats

Experiments on 22 cases from 11 real-world software systems/environments confirm that MMO outperforms single-objective counterparts on 82% of cases with up to 2.09× speedup. For 68% of cases, the new normalization enables MMO to outperform prior work under pre-tuned best weights, saving resources. The MMO model consolidates recent tuning tools on 68% of cases with up to 1.22× speedup.

Quotes

Key Insights Distilled From

MMO

by Pengzhou Che... at arxiv.org 03-18-2024

https://arxiv.org/pdf/2112.07303.pdf

Deeper Inquiries

How can the MMO model be adapted for different types of software systems?

The MMO model can be adapted for different types of software systems by considering the specific characteristics and requirements of each system. This adaptation involves customizing the target and auxiliary performance objectives based on what is important for that particular system. For example, in a real-time processing system like APACHE STORM, latency might be a critical performance metric to optimize, while in a machine learning system, accuracy could take precedence. By adjusting the objectives and weights in the MMO model to align with the unique needs of each software system, it can effectively guide the search towards finding optimal configurations without being trapped in local optima.

What are the limitations of relying on normalization methods to address sensitivity in parameter settings?

While normalization methods like those used in the FSE work can help reduce sensitivity in parameter settings by scaling performance objectives to comparable ranges, they have limitations. One limitation is that these methods rely on historical data or initial estimates of objective scales which may not accurately reflect true variations during optimization. Additionally, normalization does not address underlying issues causing sensitivity such as complex interactions between configuration options or uncertain correlations between performance metrics. Normalization also adds complexity to the optimization process and may introduce biases if not applied correctly.

How can the concept of multi-objectivization be applied beyond software configuration tuning?

The concept of multi-objectivization can be applied beyond software configuration tuning to various optimization problems where multiple conflicting objectives need to be considered simultaneously. In fields like engineering design, financial portfolio management, logistics planning, and resource allocation, decision-makers often face trade-offs between different goals (e.g., cost vs efficiency). By using multi-objective optimization techniques similar to those employed in software configuration tuning but tailored to specific domain requirements, stakeholders can make informed decisions that balance competing objectives effectively. Multi-objectivization enables a more comprehensive analysis of solutions by considering diverse criteria simultaneously rather than focusing solely on one aspect at a time.