Fail-Slow Hardware Failure Bug Analysis and Detection for Cloud Systems
Fail-slow hardwares are still running and functional, but in a degraded mode, thus slower than their expected performance. Bugs triggered by fail-slow hardwares cause severe cloud system failures. Existing testing tools fail to efficiently detect these bugs due to overlooking their characteristics. In order to address this problem, this paper provides a bug study that analyzes 48 real-world fail-slow hardware failures from typical cloud systems. We observe that (1) fail-slow hardwares make high-level software components vulnerable, including synchronized and timeout mechanisms; (2) the fine granularity of fail-slow hardwares is necessary to trigger these bugs. Based on these two observations, we propose Sieve, a fault injection testing framework for detecting fail-slow hardware failure bugs. Sieve statically analyzes target system codes to identify synchronized and timeout-protected I/O operations as candidate fault points and instruments hooks before candidate fault points to enable fail-slow hardware injection. To efficiently explore candidate fault points, Sieve adopts grouping and context-sensitive injection strategies. We have applied Sieve to three widely deployed cloud systems, i.e., ZooKeeper, Kafka, and HDFS. Sieve has detected six unknown bugs, two of which have been confirmed.