gigiProfiler: Diagnosing Performance Issues by Uncovering Application Resource Bottlenecks

2025-07-08

AI Summary

Modern software systems frequently exhibit application-level resource contention bottlenecks—such as blocking on custom application events—that evade detection by conventional performance profilers due to complex dependencies and bespoke resource management. To address this, we propose OmniResource Profiling, the first method to jointly leverage system-level metrics and application-level event-waiting relationships. It employs a lightweight LLM-assisted static analysis to automatically identify custom resources and cross-execution-trace runtime variable comparison for precise root-cause localization. Evaluated on 12 known performance issues across five real-world applications, OmniResource achieves 100% diagnostic accuracy and uncovers two previously undetected bottlenecks. Crucially, it requires no intrusive instrumentation, balancing high precision with practical deployability. This work delivers the first end-to-end solution for application-level resource contention analysis.

Technology Category

• Search and Optimization: Applications
• Multiagent Systems: Applications
• Machine Learning: Hardware-aware ML

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Web performance, measurement, and characterization
• Security and Privacy: Tracking, profiling, and countermeasures against them
• Web Mining and Content Analysis: Web measurements

Abstract

Diagnosing performance bottlenecks in modern software is essential yet challenging, particularly as applications become more complex and rely on custom resource management policies. While traditional profilers effectively identify execution bottlenecks by tracing system-level metrics, they fall short when it comes to application-level resource contention caused by waiting for application-level events. In this work, we introduce OmniResource Profiling, a performance analysis approach that integrates system-level and application-level resource tracing to diagnose resource bottlenecks comprehensively. gigiProfiler, our realization of OmniResource Profiling, uses a hybrid LLM-static analysis approach to identify application-defined resources offline and analyze their impact on performance during buggy executions to uncover the performance bottleneck. gigiProfiler then samples and records critical variables related to these bottleneck resources during buggy execution and compares their value with those from normal executions to identify the root causes. We evaluated gigiProfiler on 12 real-world performance issues across five applications. gigiProfiler accurately identified performance bottlenecks in all cases. gigiProfiler also successfully diagnosed the root causes of two newly emerged, previously undiagnosed problems, with the findings confirmed by developers.

Problem

• Diagnosing performance bottlenecks in complex applications
• Identifying application-level resource contention issues
• Uncovering root causes of performance problems

Innovation

• Integrates system and application resource tracing
• Uses hybrid LLM-static analysis approach
• Samples critical variables for root cause analysis