Abstract
The complexity and scale of high-performance computer systems are rapidly increasing, so fault tolerance is becoming a critical challenge. In this paper, we consider the impact of multiple proactive actions on proactive fault tolerance and periodic checkpointing. We extended Aupy’s model in the presence of multiple proactive actions, including proactive checkpointing and task migration. We then propose optimal strategies for deciding when to trust predictions, and provide algorithms for the optimal storage interval for periodic checkpointing. The results show that the proposed method can significantly improve system productivity. Our case study indicates that the recall of the predictor is more important for small platforms, and that precision becomes increasingly important as the scale of the system increases.
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Notes
The results obtained via the Weibull distribution can be found in http://good.gd/3262191.htm.
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Acknowledgments
The authors thank Tang from the Illinois Institute of Technology for making the log file of Intrepid available. We would like to thank the reviewers for their comments and suggestions, which greatly helped improve this paper.
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Zhu, L., Gu, J., Wang, Y. et al. Optimizing the fault-tolerance overheads of HPC systems using prediction and multiple proactive actions. J Supercomput 71, 3668–3694 (2015). https://doi.org/10.1007/s11227-015-1458-0
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DOI: https://doi.org/10.1007/s11227-015-1458-0