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Two-Level Incremental Checkpoint Recovery Scheme for Reducing System Total Overheads

机译:降低系统总开销的两级增量检查点恢复方案

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摘要

Long-running applications are often subject to failures. Once failures occur, it will lead to unacceptable system overheads. The checkpoint technology is used to reduce the losses in the event of a failure. For the two-level checkpoint recovery scheme used in the long-running tasks, it is unavoidable for the system to periodically transfer huge memory context to a remote stable storage. Therefore, the overheads of setting checkpoints and the re-computing time become a critical issue which directly impacts the system total overheads. Motivated by these concerns, this paper presents a new model by introducing i-checkpoints into the existing two-level checkpoint recovery scheme to deal with the more probable failures with the smaller cost and the faster speed. The proposed scheme is independent of the specific failure distribution type and can be applied to different failure distribution types. We respectively make analyses between the two-level incremental and two-level checkpoint recovery schemes with the Weibull distribution and exponential distribution, both of which fit with the actual failure distribution best. The comparison results show that the total overheads of setting checkpoints, the total re-computing time and the system total overheads in the two-level incremental checkpoint recovery scheme are all significantly smaller than those in the two-level checkpoint recovery scheme. At last, limitations of our study are discussed, and at the same time, open questions and possible future work are given.
机译:长时间运行的应用程序经常会出现故障。一旦发生故障,将导致无法接受的系统开销。检查点技术用于减少发生故障时的损失。对于长时间运行的任务中使用的两级检查点恢复方案,系统不可避免地要定期将大内存上下文定期传输到远程稳定存储。因此,设置检查点的开销和重新计算时间成为一个关键问题,直接影响系统的总开销。出于这些担忧的考虑,本文通过将i-checkpoints引入到现有的两级checkpoint恢复方案中,提出了一种新模型,以更低的成本和更快的速度处理更多可能的故障。所提出的方案与特定的故障分布类型无关,并且可以应用于不同的故障分布类型。我们分别对具有威布尔分布和指数分布的两级增量和两级检查点恢复方案进行分析,这两种方法都最适合实际的故障分布。比较结果表明,两级增量式检查点恢复方案中设置检查点的总开销,重新计算的总时间和系统的总开销均显着小于两级检查点恢复方案中的开销。最后,讨论了我们研究的局限性,同时给出了未解决的问题和可能的未来工作。

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