Hardness, Structural Knowledge, and Opportunity: An Analytical Framework for Modular Performance Modeling

2025-09-13

AI Summary

Configuration space explosion complicates performance impact modeling, while gray-box approaches rely on structural knowledge (e.g., module execution graphs) to improve model accuracy—yet the mechanisms by which structural features (e.g., number of modules or configuration options) and structural knowledge influence modeling difficulty and optimization potential remain unclear. Method: We formally define “modeling hardness” and “improvement opportunity,” establishing an analytical framework and matrix to quantify the interplay among system structural complexity, structural knowledge level, and modeling benefit. Controlled experiments on synthetic systems integrate module execution graph analysis with gray-box modeling. Contribution/Results: We identify module count and configuration option count as dominant determinants of modeling hardness. Under high hardness, strong structural knowledge significantly increases improvement opportunity. Structural knowledge primarily enhances ranking accuracy, whereas hardness predominantly degrades prediction accuracy. Our findings provide theoretical foundations and strategic guidance for allocating structural knowledge investment according to specific modeling objectives.

Technology Category

• Knowledge Representation and Reasoning: Computational Complexity of Reasoning
• Machine Learning: Structured Learning
• Reasoning under Uncertainty: Graphical Models

Application Category

• User Modeling, Personalization and Recommendation: Attacks and countermeasures in recommendation systems
• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• Graph Algorithms and Modeling for the Web: Foundation models and LLMs for Web-related graphs

Abstract

Performance-influence models are beneficial for understanding how configurations affect system performance, but their creation is challenging due to the exponential growth of configuration spaces. While gray-box approaches leverage selective "structural knowledge" (like the module execution graph of the system) to improve modeling, the relationship between this knowledge, a system's characteristics (we call them "structural aspects"), and potential model improvements is not well understood. This paper addresses this gap by formally investigating how variations in structural aspects (e.g., the number of modules and options per module) and the level of structural knowledge impact the creation of "opportunities" for improved "modular performance modeling". We introduce and quantify the concept of modeling "hardness", defined as the inherent difficulty of performance modeling. Through controlled experiments with synthetic system models, we establish an "analytical matrix" to measure these concepts. Our findings show that modeling hardness is primarily driven by the number of modules and configuration options per module. More importantly, we demonstrate that both higher levels of structural knowledge and increased modeling hardness significantly enhance the opportunity for improvement. The impact of these factors varies by performance metric; for ranking accuracy (e.g., in debugging task), structural knowledge is more dominant, while for prediction accuracy (e.g., in resource management task), hardness plays a stronger role. These results provide actionable insights for system designers, guiding them to strategically allocate time and select appropriate modeling approaches based on a system's characteristics and a given task's objectives.

Problem

• Investigating how structural aspects and knowledge affect modular performance modeling opportunities
• Quantifying modeling hardness driven by module and option counts
• Analyzing the varying impact of knowledge and hardness on different performance metrics

Innovation

• Analytical matrix measures modeling hardness and opportunities
• Structural knowledge and hardness enhance improvement opportunities
• Guidance for system designers on modeling approach selection