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S3 Lab - Software & Systems Security Laboratory

IntroPerf: Transparent Context-Sensitive Multi-Layer Performance Inference using System Stack Traces

Chung Hwan Kim, Junghwan Rhee, Hui Zhang, Nipun Arora, Guofei Jiang, Xiangyu Zhang, and Dongyan Xu

Proceedings of the 2014 ACM International Conference on Measurement and Modeling of Computer Systems (SIGMETRICS) 2014.

Operating Systems, Program Analysis


Performance bugs are frequently observed in commodity software. While profilers or source code-based tools can be used at development stage where a program is diagnosed in a well-defined environment, many performance bugs survive such a stage and affect production runs. OS kernel-level tracers are commonly used in post-development diagnosis due to their independence from programs and libraries; however, they lack detailed program-specific metrics to reason about performance problems such as function latencies and program contexts. In this paper, we propose a novel performance inference system, called IntroPerf, that generates fine-grained performance information - like that from application profiling tools - transparently by leveraging OS tracers that are widely available in most commodity operating systems. With system stack traces as input, IntroPerf enables transparent context-sensitive performance inference, and diagnoses application performance in a multi-layered scope ranging from user functions to the kernel. Evaluated with various performance bugs in multiple open source software projects, IntroPerf automatically ranks potential internal and external root causes of performance bugs with high accuracy without any prior knowledge about or instrumentation on the subject software. Our results show IntroPerf’s effectiveness as a lightweight performance introspection tool for post-development diagnosis.