3-Dimensional Root Cause Diagnosis via Co-analysis

With the growth of system size and complexity, reliability has become a major concern for large-scale systems. Up-on the occurrence of failure, system administrators typically trace the events in Reliability, Availability, and Serviceabil-ity (RAS) logs for root cause diagnosis. However, RAS log only contains limited diagnosis information. Moreover, the manual processing is time-consuming, error-prone, and not scalable. To address the problem, in this paper we present an automated root cause diagnosis mechanism for large-scale HPC systems. Our mechanism examines multiple logs to provide a 3-D fine-grained root cause analysis. Here, 3-D means that our analysis will pinpoint the failure layer, the time, and the location of the event that causes the problem. We evaluate our mechanism by means of real logs col-lected from a production IBM Blue Gene/P system at Oak Ridge National Laboratory. It successfully identifies failure layer information for 219 failures during 23-month period. Furthermore, it effectively identifies the triggering events with time and location information, even when the triggering events occur hundreds of hours before the resulting failures