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Effective straggler mitigation: attack of the clones

Published: 02 April 2013 Publication History

Abstract

Small jobs, that are typically run for interactive data analyses in datacenters, continue to be plagued by disproportionately long-running tasks called stragglers. In the production clusters at Facebook and Microsoft Bing, even after applying state-of-the-art straggler mitigation techniques, these latency sensitive jobs have stragglers that are on average 8 times slower than themedian task in that job. Such stragglers increase the average job duration by 47%. This is because current mitigation techniques all involve an element of waiting and speculation. We instead propose full cloning of small jobs, avoiding waiting and speculation altogether. Cloning of small jobs only marginally increases utilization because workloads show that while the majority of jobs are small, they only consume a small fraction of the resources. The main challenge of cloning is, however, that extra clones can cause contention for intermediate data. We use a technique, delay assignment, which efficiently avoids such contention. Evaluation of our system, Dolly, using production workloads shows that the small jobs speedup by 34% to 46% after state-of-the-artmitigation techniques have been applied, using just 5% extra resources for cloning.

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    cover image Guide Proceedings
    nsdi'13: Proceedings of the 10th USENIX conference on Networked Systems Design and Implementation
    April 2013
    546 pages

    Sponsors

    • VMware
    • Akamai: Akamai
    • Google Inc.
    • NSF
    • Facebook: Facebook

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    USENIX Association

    United States

    Publication History

    Published: 02 April 2013

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    • (2023)Extending and Programming the NVMe I/O Determinism Interface for Flash ArraysACM Transactions on Storage10.1145/356842719:1(1-33)Online publication date: 11-Jan-2023
    • (2022)Improving the Performance of Heterogeneous Data Centers through RedundancyACM SIGMETRICS Performance Evaluation Review10.1145/3543516.345627449:1(55-56)Online publication date: 7-Jun-2022
    • (2021)Redundancy techniques for straggler mitigation in distributed optimization and learningThe Journal of Machine Learning Research10.5555/3322706.336201320:1(2619-2665)Online publication date: 9-Mar-2021
    • (2021)Improving the Performance of Heterogeneous Data Centers through RedundancyProceedings of the ACM on Measurement and Analysis of Computing Systems10.1145/34283334:3(1-29)Online publication date: 15-Jun-2021
    • (2021)Improving the Performance of Heterogeneous Data Centers through RedundancyAbstract Proceedings of the 2021 ACM SIGMETRICS / International Conference on Measurement and Modeling of Computer Systems10.1145/3410220.3456274(55-56)Online publication date: 31-May-2021
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