USENIX Association 11 th USENIX Symposium on Networked Systems Design and Implementation 289 GRASS : Trimming Stragglers in Approximation Analytics
USENIX Association 11 th USENIX Symposium on Networked Systems Design and Implementation 289 GRASS : Trimming Stragglers in Approximation Analytics
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发表时间：
2014
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通讯作者：
Ganesh Ananthanarayanan;Michael Chien-Chun Hung;Xiaoqi Ren;I. Stoica;A. Wierman;Minlan Yu
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作者：
Ganesh Ananthanarayanan;Michael Chien-Chun Hung;Xiaoqi Ren;I. Stoica;A. Wierman;Minlan Yu
在大数据分析中，及时的结果，即使只基于部分数据，通常也足够好。出于这个原因，近似作业，有截止日期或误差范围，只需要完成其任务的一个子集，预计将主导大数据工作负载。离散任务是设计近似数据分析框架时的一个重要障碍，广泛采用的处理方法是推测执行。在本文中，我们提出了GRASS，它小心地使用投机，以减轻近似工作中的掉队者的影响。GRASS的设计是基于第一性原理的影响分析推测。GRASS巧妙地平衡了改进近似目标的即时性和使用额外资源进行投机的长期影响。在200个节点的EC2集群中对Facebook和Microsoft Bing的生产工作负载进行的评估表明，GRASS将截止日期作业的准确性提高了47%，并将错误作业的速度提高了38%。GRASS的设计还加快了精确计算（零误差范围），使其成为一个统一的解决方案，以减少落伍者。
In big data analytics, timely results, even if based on only part of the data, are often good enough. For this reason, approximation jobs, which have deadline or error bounds and require only a subset of their tasks to complete, are projected to dominate big data workloads. Straggler tasks are an important hurdle when designing approximate data analytic frameworks, and the widely adopted approach to deal with them is speculative execution. In this paper, we present GRASS, which carefully uses speculation to mitigate the impact of stragglers in approximation jobs. GRASS’s design is based on first principles analysis of the impact of speculation. GRASS delicately balances immediacy of improving the approximation goal with the long term implications of using extra resources for speculation. Evaluations with production workloads from Facebook and Microsoft Bing in an EC2 cluster of 200 nodes shows that GRASS increases accuracy of deadline-bound jobs by 47% and speeds up error-bound jobs by 38%. GRASS’s design also speeds up exact computations (zero error-bound), making it a unified solution for straggler mitigation.