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Application-aware deadline constraint job scheduling mechanism on large-scale computational grid

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  • Xiaoyong Tang
  • Xiaoyi Liao

Abstract

Recently, computational Grids have proven to be a good solution for processing large-scale, computation intensive problems. However, the heterogeneity, dynamics of resources and diversity of applications requirements have always been important factors affecting their performance. In response to these challenges, this work first builds a Grid job scheduling architecture that can dynamically monitor Grid computing center resources and make corresponding scheduling decisions. Second, a Grid job model is proposed to describe the application requirements. Third, this paper studies the characteristics of commercial interconnection networks used in Grids and forecast job transmission time. Fourth, this paper proposes an application-aware job scheduling mechanism (AJSM) that includes periodic scheduling flow and a heuristic application-aware deadline constraint job scheduling algorithm. The rigorous performance evaluation results clearly demonstrate that the proposed application-aware job scheduling mechanism can successful schedule more Grid jobs than the existing algorithms. For successful scheduled jobs, our proposed AJSM method is the best algorithm for job average processing time and makespan.

Suggested Citation

  • Xiaoyong Tang & Xiaoyi Liao, 2018. "Application-aware deadline constraint job scheduling mechanism on large-scale computational grid," PLOS ONE, Public Library of Science, vol. 13(11), pages 1-19, November.
    • Handle: RePEc:plo:pone00:0207596
    DOI: 10.1371/journal.pone.0207596
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    References listed on IDEAS

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    1. Tang, Jinjun & Yang, Yifan & Qi, Yong, 2018. "A hybrid algorithm for Urban transit schedule optimization," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 512(C), pages 745-755.
    2. Tang, Jinjun & Liang, Jian & Zhang, Shen & Huang, Helai & Liu, Fang, 2018. "Inferring driving trajectories based on probabilistic model from large scale taxi GPS data," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 506(C), pages 566-577.
    3. Yajie Zou & John E. Ash & Byung-Jung Park & Dominique Lord & Lingtao Wu, 2018. "Empirical Bayes estimates of finite mixture of negative binomial regression models and its application to highway safety," Journal of Applied Statistics, Taylor & Francis Journals, vol. 45(9), pages 1652-1669, July.
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    Cited by:

    1. Alaa A. Abdallat & Arwa I. Alahmad & Duaa A. AlSahebAlT amimi & Jaber A. AlWidian, 2019. "Hadoop MapReduce Job Scheduling Algorithms Survey and Use Cases," Modern Applied Science, Canadian Center of Science and Education, vol. 13(7), pages 1-38, July.

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