Production-Run Software Failure Diagnosis via Hardware Performance Counters
Sequential and concurrency bugs are widespread in deployed software. They cause severe failures and huge financial loss during production runs. Tools that diagnose production-run failures with low overhead are needed. The state-of-the-art diagnosis techniques use software instrumentation to sample program properties at run time and use off-line statistical analysis to identify properties most correlated with failures. Although promising, these techniques suffer from high run-time overhead, which is sometimes over 100%, for concurrency-bug failure diagnosis and hence are not suitable for production-run usage. We present PBI, a system that uses existing hardware performance counters to diagnose production-run failures caused by sequential and concurrency bugs with low overhead. PBI is designed based on several key observations. First, a few widely supported performance counter events can reflect a wide variety of common software bugs and can be monitored by hardware with almost no overhead. Second, the counter overflow interrupt supported by existing hardware and operating systems provides a natural and effective mechanism to conduct event sampling at user level. Third, the noise and non-determinism in interrupt delivery complements well with statistical processing. We evaluate PBI using 13 real-world concurrency and sequential bugs from representative open-source server, client, and utility programs, and 10 bugs from a widely used software-testing benchmark. Quantitatively, PBI can effectively diagnose failures caused by these bugs with a small overhead that is never higher than 10 %. Qualitatively, PBI does not require any change to software and presents a novel use of existing hardware performance counters.
failure diagnosis performance counters concurrency bugs production run reliability languages measurement