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A Parallel Job Execution Time Estimation Approach Based on User Submission Patterns within Computational Grids

  • Scheduling performance in computational grid can potentially benefit a lot from accurate execution time estimation for parallel jobs. Most existing approaches for the parallel job execution time estimation, however, require ample past job traces and the explicit correlations between the job execution time and the outer layout parameters such as the consumed processor numbers, the user-estimated execution time and the job ID, which are hard to obtain or reveal. This paper presents and evaluates a novel execution time estimation approach for parallel jobs, the user-behavior clustering for execution time estimation, which can give more accurate execution time estimation for parallel jobs through exploring the job similarity and revealing the user submission patterns. Experiment results show that compared to the state-of-art algorithms, our approach can improve the accuracy of the job execution time estimation up to 5.6 %, meanwhile the time that our approach spends on calculation can be reduced up to 3.8 %.

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Author:Feng Liang, Yunzhen Liu, Hai Liu, Shilong Ma, Bettina SchnorGND
ISSN:0885-7458 (print)
ISSN:1573-7640 (online)
Parent Title (English):International journal of parallel programming
Place of publication:New York
Document Type:Article
Year of first Publication:2015
Year of Completion:2015
Release Date:2017/03/27
Tag:Computational grid; Parallel job execution time estimation; User submission pattern
First Page:440
Last Page:454
Funder:State Key Laboratory for Software Development Environment in China [SKLSDE-2013ZX-11]; Special Program for Seism-Scientific Research in Public Interest "Research in Online Processing Technologies for Seismological Precursory Network Dynamic Monitoring and Products" [201008002]
Organizational units:Mathematisch-Naturwissenschaftliche Fakultät / Institut für Informatik und Computational Science
Peer Review:Referiert
Institution name at the time of publication:Mathematisch-Naturwissenschaftliche Fakultät / Institut für Informatik