CrashTuner

Proceedings of the 27th ACM Symposium on Operating Systems Principles Pub Date : 2019-10-27 DOI:10.1145/3341301.3359645

Jie Lu, Chen Liu, Lian Li, Xiaobing Feng, Feng Tan, Jun Yang, Liang You

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引用次数: 19

Abstract

Crash-recovery bugs (bugs in crash-recovery-related mechanisms) are among the most severe bugs in cloud systems and can easily cause system failures. It is notoriously difficult to detect crash-recovery bugs since these bugs can only be exposed when nodes crash under special timing conditions. This paper presents CrashTuner, a novel fault-injection testing approach to combat crash-recovery bugs. The novelty of CrashTuner lies in how we identify fault-injection points (crash points) that are likely to expose errors. We observe that if a node crashes while accessing meta-info variables, i.e., variables referencing high-level system state information (e.g., an instance of node or task), it often triggers crash-recovery bugs. Hence, we identify crash points by automatically inferring meta-info variables via a log-based static program analysis. Our approach is automatic and no manual specification is required. We have applied CrashTuner to five representative distributed systems: Hadoop2/Yarn, HBase, HDFS, ZooKeeper, and Cassandra. CrashTuner can finish testing each system in 17.39 hours, and reports 21 new bugs that have never been found before. All new bugs are confirmed by the original developers and 16 of them have already been fixed (14 with our patches). These new bugs can cause severe damages such as cluster down or start-up failures.

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Proceedings of the 27th ACM Symposium on Operating Systems Principles

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