Simple Policies for Multiresource Job Scheduling

Data center workloads are composed of multiresource jobs requiring a variety of computational resources including CPU cores, memory, disk space, and hardware accelerators. Modern servers can run multiple jobs in parallel, but a set of jobs can only run in parallel if the server has sufficient resources to satisfy the demands of each job. It is generally hard to find sets of jobs that perfectly utilize all server resources, and choosing the wrong set of jobs can lead to low resource utilization. This raises the question of how to allocate resources across a stream of arriving multiresource jobs to minimize the mean response time across jobs - the mean time from when a job arrives to the system until it is complete.

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Publication Details

Journal
ACM SIGMETRICS Performance Evaluation Review
Published
2024-09-05
DOI
https://doi.org/10.1145/3695411.3695422
Citations
2
Primary Topic
Advanced Wireless Network Optimization
Type
article
Field-Weighted Citation Impact
0.42
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article

Simple Policies for Multiresource Job Scheduling

Isaac Grosof, Zhongrui Chen, Benjamin Berg
2 citations
ACM SIGMETRICS Performance Evaluation Review
Advanced Wireless Network Optimization
0.42
article

Simple Policies for Multiresource Job Scheduling

Isaac Grosof, Zhongrui Chen, Benjamin Berg
article en
2 citations

Abstract

Data center workloads are composed of multiresource jobs requiring a variety of computational resources including CPU cores, memory, disk space, and hardware accelerators. Modern servers can run multiple jobs in parallel, but a set of jobs can only run in parallel if the server has sufficient resources to satisfy the demands of each job. It is generally hard to find sets of jobs that perfectly utilize all server resources, and choosing the wrong set of jobs can lead to low resource utilization. This raises the question of how to allocate resources across a stream of arriving multiresource jobs to minimize the mean response time across jobs - the mean time from when a job arrives to the system until it is complete.

ACM SIGMETRICS Performance Evaluation ReviewVol. 52(2)
University of North Carolina at Chapel Hill (US), University of Illinois Urbana-Champaign (US)
Openalex Percentile: Top 38%
Advanced Wireless Network Optimization
0.42
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