PASD: A Performance Analysis Approach Through the Statistical Debugging of Kernel Events

Dynamic performance analysis plays a crucial role in optimizing systems and identifying performance bottlenecks. Traditional software debugging methods frequently encounter difficulties when trying to pinpoint performance problems in complex software settings. This is often because performance issues remain hidden during the code execution within debugging tools or under certain run-time circumstances, making them challenging to identify and address. This paper introduces PASD (Performance Analysis through Statistical Debugging), a dynamic performance analysis approach based on statistical debugging of kernel-level trace events. Importantly, this approach requires no application code instrumentation and purely utilizes operating system kernel trace events for analysis. PASD collects kernel trace events generated during software execution and utilizes heuristics to analyze their performance issues and the root-causes. Through statistical debugging techniques, PASD identifies the most important functions correlated with performance problems. It notably does so without disrupting the software’s normal functions and ensuring that any issues are detected in the software’s typical operating conditions, thus avoiding additional complexity in the debugging process. We have conducted two empirical studies to assess the effectiveness of PASD on performance issues in the Firefox web browser as well as the ‘ls’ tool (a common utility in Unix-like systems). Our experiments demonstrate that PASD successfully identifies performance issues and their causes in software without prior knowledge of the architecture or source code instrumentation. By providing an overview of software behavior through the kernel-level, our proposed method can aid developers and testers in quickly pinpointing performance problems in the source code. This, in turn, can result in improved software quality, increased user satisfaction, and the prevention of critical system failures.

I. Introduction

Performance debugging, the process of identifying and diagnosing bottlenecks within software systems, has become increasingly vital in today’s computing landscape [1]. As software architectures grow more complex and diverse deployment environments become the norm, performance issues can greatly undermine user experience and overall system efficiency. For instance, a major e-commerce platform may experience a slowdown during peak shopping periods [2], leading to longer page load times. This seemingly minor issue can have a significant impact on user experience, potentially leading to customer dissatisfaction and loss of revenue. In a more critical context, consider a healthcare system where a delay in processing patient data could result in delayed diagnoses or treatment, with potentially life-threatening consequences. These examples underscore the importance of effective performance debugging in maintaining the efficiency, reliability, and user satisfaction of software systems. However, as software architectures become increasingly complex and diverse, traditional debugging techniques often fail to identify and resolve performance issues [3].