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UID:submissions.supercomputing.org_SC23_sess299_spostu105@linklings.com
SUMMARY:A Reinforcement Learning-Based Backfilling Strategy for HPC Batch 
 Jobs
DESCRIPTION:Elliot Kolker-Hicks (University of North Carolina, Charlotte)\
 n\nHigh Performance Computing (HPC) systems are essential for various scie
 ntific fields, and effective job scheduling is crucial for their performan
 ce. Traditional backfilling techniques, such as EASY-backfilling, rely on 
 user-submitted runtime estimates, which can be inaccurate and lead to subo
 ptimal scheduling. This poster presents RL-Backfiller, a novel reinforceme
 nt learning (RL) based approach to improve HPC job scheduling. Our method 
 incorporates RL to make better backfilling decisions, independent of user-
 submitted runtime estimates. We trained RL-Backfiller on the synthetic Lub
 lin-256 workload and tested it on the real SDSC-SP2 1998 workload. We show
  how RLBackfilling can learn effective backfilling strategies and outperfo
 rm traditional EASY-backfilling and other heuristic combinations via trial
 -and-error on existing job traces. Our evaluation results show up to 17x b
 etter scheduling performance (based on average bounded job slowdown) compa
 red to EASY-backfilling\n\nTag: Artificial Intelligence/Machine Learning, 
 Algorithms, Applications, Architecture and Networks, Cloud Computing, Dist
 ributed Computing, Data Analysis, Visualization, and Storage, Performance 
 Measurement, Modeling, and Tools, Programming Frameworks and System Softwa
 re\n\nRegistration Category: Tech Program Reg Pass, Exhibits Reg Pass\n\n
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