Proactive process-level live migration in HPC environments
Proceedings of the 2008 ACM/IEEE conference on Supercomputing
An analysis of clustered failures on large supercomputing systems
Journal of Parallel and Distributed Computing
A study of dynamic meta-learning for failure prediction in large-scale systems
Journal of Parallel and Distributed Computing
Online event correlations analysis in system logs of large-scale cluster systems
NPC'10 Proceedings of the 2010 IFIP international conference on Network and parallel computing
Architecting dependable systems with proactive fault management
Architecting dependable systems VII
Proactive process-level live migration and back migration in HPC environments
Journal of Parallel and Distributed Computing
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The demand for more computational power in science and engineering has spurred the design and deployment of ever-growing cluster systems. Even though the individual components used in these systems are highly reliable, the presence of large number of components inevitably increases the failure probability of such systems. Successful prediction of potential failures can greatly enhance various fault tolerance mechanisms used in large clusters, thereby mitigating the adverse impact of failures on system productivity and total cost of ownership. In this paper, we present a three-phase failure predictor to automatically process RAS events and further discover failure patterns for prediction in Blue Gene/L systems. In particular, this paper explores the use of metalearning to adaptively integrate base methods with the goal to boost prediction accuracy. Experiments with two RAS logs collected from Blue Gene/L systems at ANL and SDSC demonstrate the effectiveness of the proposed failure predictor.