sponsored byACMIEEE The International Conference for High Performance 
Computing, Networking, Storage and Analysis
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SCHEDULE: NOV 15-20, 2015

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Elastic Job Bundling: An Adaptive Resource Request Strategy for Large-Scale Parallel Applications

SESSION: Cloud Resource Management


EVENT TAG(S): Clouds and Distributed Computing

TIME: 11:00AM - 11:30AM


AUTHOR(S):Feng Liu, Jon B. Weissman



In today’s batch queue HPC cluster systems, the user submits a job requesting a fixed number of processors. The system will not start the job until all of the requested resources become available simultaneously. When cluster workload is high, large sized jobs will experience long waiting time due to this policy. In this paper, we propose a new approach that dynamically decomposes a large job into smaller ones to reduce waiting time, and lets the application expand across multiple subjobs while continuously achieving progress. This approach has three benefits: (i) application turnaround time is reduced, (ii) system fragmentation is diminished, and (iii) fairness is promoted. Our approach does not depend on job queue time prediction but exploits available backfill opportunities. Simulation results have shown that our approach can reduce application mean turnaround time by up to 48%.

Chair/Author Details:

Brent Welch (Chair) - Google|

Feng Liu - University of Minnesota Twin Cities

Jon B. Weissman - University of Minnesota Twin Cities

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Paper provided by the ACM Digital Library

Paper also available from IEEE Computer Society