3-Dimensional root cause diagnosis via co-analysis
Proceedings of the 9th international conference on Autonomic computing
Heterogeneity and dynamicity of clouds at scale: Google trace analysis
Proceedings of the Third ACM Symposium on Cloud Computing
When is multi-version checkpointing needed?
Proceedings of the 3rd Workshop on Fault-tolerance for HPC at extreme scale
Failure analysis of distributed scientific workflows executing in the cloud
Proceedings of the 8th International Conference on Network and Service Management
Integrating dynamic pricing of electricity into energy aware scheduling for HPC systems
SC '13 Proceedings of the International Conference on High Performance Computing, Networking, Storage and Analysis
Failure prediction for HPC systems and applications: Current situation and open issues
International Journal of High Performance Computing Applications
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With the growth of system size and complexity, reliability has become of paramount importance for petascale systems. Reliability, Availability, and Serviceability (RAS) logs have been commonly used for failure analysis. However, analysis based on just the RAS logs has proved to be insufficient in understanding failures and system behaviors. To overcome the limitation of this existing methodologies, we analyze the Blue Gene/P RAS logs and the Blue Gene/P job logs in a cooperative manner. From our co-analysis effort, we have identified a dozen important observations about failure characteristics and job interruption characteristics on the Blue Gene/P systems. These observations can significantly facilitate the research in fault resilience of large-scale systems.