Introperf: Transparent context-sensitive multilayer performance inference using system stack traces

Performance bugs are frequently observed in commodity soft-ware. 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 pro-gram contexts. In this paper, we propose a novel perfor-mance inference system, called IntroPerf, that generates fine-grained performance information – like that from ap-plication profiling tools – transparently by leveraging OS tracers that are widely available in most commodity operat-ing 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. Evalu-ated with various performance bugs in multiple open source software projects, IntroPerf automatically ranks poten-tial 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 in-trospection tool for post-development diagnosis