Large-scale distributed systems are prone to frequent failures, which could be caused by a variety of factors related to network, hardware, and software problems. Any downtime due to failures, whatever the cause, can lead to large disruptions and huge losses. Identifying the location and cause of a failure is critical for the reliability and availability of such systems. However, identifying the actual cause of failures in such systems is a challenging task due to their large scale and variety of failure causes.
In this work, we try to understand failures in a large-scale system through a two-step methodology: (i) classifying failures based on their statistical properties, and (ii) using additional monitoring data to explain these failures. We illustrate our methodology through a systematic study of failures in PlanetLab over a 3-month period. Our results show that most of the failures that required restarting a node were of small size and lasted for long durations. We also found that incorporating geographic information into our analysis enabled us to find site-wise correlated failures. We were also able to explain some failures by using error-message information collected by the monitoring nodes, and some of short-lived failures by transient CPU overloads on machines.
Jain, Sourabh; Prinja, Rohini; Chandra, Abhishek; Zhang, Zhi-Li.
Failure Classification and Inference in Large-Scale Systems: A Systematic Study of Failures in PlanetLab.
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