Lawrence Berkeley National Laboratory

Failure Prediction has long known to be a challenging problem. With the evolving trend of technology and growing complexity of high-performance cloud data centre infrastructure, focusing on failure becomes very vital particularly when designing systems for the next generation. The traditional runtime fault-tolerance (FT) techniques such as data replication and periodic check-pointing are not very effective to handle the current state of the art emerging computing systems. This has necessitated the urgent need for a robust system with an in-depth understanding of system and component failures as well as the ability to predict accurate potential future system failures. In this paper, we studied data in-production-faults recorded within a five years period from the National Energy Research Scientific computing centre (NERSC). Using the data collected from the Computer Failure Data Repository (CFDR), we developed an effective failure prediction model focusing on high-performance cloud data centre infrastructure. Using the Auto-Regressive Moving Average(ARMA), our model was able to predict potential future failures in the system. Our results also show a failure prediction accuracy of 95%, which is good. We thus believe that our strategy is practical and can be adapted to use in existing real-time systems.