The Case for Phase-Aware Scheduling of Parallelizable Jobs
Authors: 
Benjamin Berg, Justin Whitehouse, Benjamin Moseley, Weina Wang, Mor Harchol-BalterAuthors Info & Claims
Pages 65 - 66
Abstract
Parallelizable workloads are ubiquitous and appear across a diverse array of modern computer systems. Data centers, supercomputers, machine learning clusters, distributed computing frameworks, and databases all process jobs designed to be parallelized across many servers or cores. Unlike the jobs in more classical models, such as the M/G/k queue, that each run on a single server, parallelizable jobs are capable of running on multiple servers simultaneously. When a job is parallelized across additional servers or cores, the job receives a speedup and can be completed more quickly.
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B. Berg, J.P. Dorsman, and M. Harchol-Balter. Towards optimality in parallel scheduling. ACM POMACS, 1(2), 2018.
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Benjamin Berg, Mor Harchol-Balter, Benjamin Moseley, Weina Wang, and Justin Whitehouse. Optimal resource allocation for elastic and inelastic jobs. In Proceedings of the 32nd ACM Symposium on Parallelism in Algorithms and Architectures, pages 75--87, 2020.
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- The Case for Phase-Aware Scheduling of Parallelizable Jobs
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Association for Computing Machinery
New York, NY, United States
Publication History
Published: 25 March 2022
Published in SIGMETRICS Volume 49, Issue 3
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