Abstract
Fault tolerance is one of the crucial challenges for HPCs to achieve exascale. In this paper, we consider the impact of the predictions that fail to precisely identify the fault-occurrence time on uncoordinated proactive checkpointing/restart (C/R). We extended Aupy’s model in the presence of the uncoordinated proactive C/R and distorted predictions. We then propose optimal strategies for deciding when to accept the predictions, and provide algorithms for the optimal storage interval for the periodic C/R. The results show that the proposed method can significantly improve the performance of the system. Furthermore, our case study indicates that the recall of the predictor is more important than precision for our system.
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Zhu, L., Gu, J., Cai, Z. (2015). Optimizing the Overheads for Uncoordinated Proactive Checkpointing. In: Wang, G., Zomaya, A., Martinez, G., Li, K. (eds) Algorithms and Architectures for Parallel Processing. ICA3PP 2015. Lecture Notes in Computer Science(), vol 9531. Springer, Cham. https://doi.org/10.1007/978-3-319-27140-8_49
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DOI: https://doi.org/10.1007/978-3-319-27140-8_49
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