Efficient Straggler Replication in Large-Scale Parallel Computing
Name
1503.03128.pdf
Description
Submitted version
Size
791 KB
Format
Adobe PDF
Checksum (MD5)
e8654b19bb074bde7d210649787024ee
Author(s)
Wang, Da
Joshi, Gauri
Wornell, Gregory W
Date Issued
2019
Journal
ACM Transactions on Modeling and Performance Evaluation of Computing Systems
Publisher
Association for Computing Machinery (ACM)
Version
Original manuscript
Abstract
© 2019 Association for Computing Machinery. In a cloud computing job with many parallel tasks, the tasks on the slowest machines (straggling tasks) become the bottleneck in the job completion. Computing frameworks such as MapReduce and Spark tackle this by replicating the straggling tasks and waiting for any one copy to finish. Despite being adopted in practice, there is little analysis of how replication affects the latency and the cost of additional computing resources. In this article, we provide a framework to analyze this latency-cost tradeoff and find the best replication strategy by answering design questions, such as (1) when to replicate straggling tasks, (2) how many replicas to launch, and (3) whether to kill the original copy or not. Our analysis reveals that for certain execution time distributions, a small amount of task replication can drastically reduce both latency and the cost of computing resources. We also propose an algorithm to estimate the latency and cost based on the empirical distribution of task execution time. Evaluations using samples in the Google Cluster Trace suggest further latency and cost reduction compared to the existing replication strategy used in MapReduce.
MIT Department
Massachusetts Institute of Technology. Signals, Information and Algorithms Laboratory
Terms of Use
Creative Commons Attribution-Noncommercial-Share Alike
Persistent DSpace Link
DOI of Published Version
https://doi.org/10.1145/3310336
