Technology Category
Search and Optimization: Sampling/Simulation-based SearchConstraint Satisfaction and Optimization: Other Foundations of Constraint SatisfactionMachine Learning: Evaluation and Analysis
Rethinking Performance Analysis for Configurable Software Systems: A Case Study from a Fitness Landscape Perspective
🤖 AI Summary
Modeling complex inter-parameter dependencies in configurable software systems remains challenging for performance tuning. Method: This paper pioneers a fitness landscape (FL) perspective to reconstruct performance analysis, modeling the high-dimensional configuration space as a structured terrain—departing from conventional isolated-point evaluation. It integrates graph-based data mining, fitness landscape analysis (FLA), and large-scale sampling (86 million configurations) across three real-world systems and 32 workload types. Contribution/Results: The study uncovers six universal landscape patterns, enabling robust identification of local optima and precise topological characterization of configuration terrain. These findings substantially deepen insights into black-box system performance, providing both a novel theoretical foundation and reusable practical guidelines for automated configuration tuning and performance modeling.
Technology Category
Application Category
📝 Abstract
Modern software systems are often highly configurable to tailor varied requirements from diverse stakeholders. Understanding the mapping between configurations and the desired performance attributes plays a fundamental role in advancing the controllability and tuning of the underlying system, yet has long been a dark hole of knowledge due to its black-box nature. While there have been previous efforts in performance analysis for these systems, they analyze the configurations as isolated data points without considering their inherent spatial relationships. This renders them incapable of interrogating many important aspects of the configuration space like local optima. In this work, we advocate a novel perspective to rethink performance analysis -- modeling the configuration space as a structured ``landscape''. To support this proposition, we designed our, an open-source, graph data mining empowered fitness landscape analysis (FLA) framework. By applying this framework to $86$M benchmarked configurations from $32$ running workloads of $3$ real-world systems, we arrived at $6$ main findings, which together constitute a holistic picture of the landscape topography, with thorough discussions about their implications on both configuration tuning and performance modeling.
Problem
Research questions and friction points this paper is trying to address.
Software Performance Tuning
Complex Interrelationships
Optimization
Innovation
Methods, ideas, or system contributions that make the work stand out.
Software Terrain Analysis
Performance Optimization
Interaction Awareness
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2024-07-02arXiv.orgCitations: 0
