KSII Transactions on Internet and Information Systems (TIIS)

Korean Society for Internet Information (한국인터넷정보학회)
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A parallel tasks Scheduling heuristic in the Cloud with multiple attributes

  • Wang, Qin (School of computer and software, Nanjing University of Information Science & Technology) ;
  • Hou, Rongtao (School of computer and software, Nanjing University of Information Science & Technology) ;
  • Hao, Yongsheng (School of computer and software, Nanjing University of Information Science & Technology) ;
  • Wang, Yin (School of public administration, Nanjing University of Information Science & Technology)
  • Received : 2017.05.11
  • Accepted : 2017.08.30
  • Published : 2018.01.31
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Abstract

There are two targets to schedule parallel jobs in the Cloud: (1) scheduling the jobs as many as possible, and (2) reducing the average execution time of the jobs. Most of previous work mainly focuses on the computing speed of resources without considering other attributes, such as bandwidth, memory and so on. Especially, past work does not consider the supply-demand condition from those attributes. Resources have different attributes, considering those attributes together makes the scheduling problem more difficult. This is the problem that we try to solve in this paper. First of all, we propose a new parallel job scheduling method based on a classification method of resources from different attributes, and then a scheduling method-CPLMT (Cloud parallel scheduling based on the lists of multiple attributes) is proposed for the parallel tasks. The classification method categories resources into different kinds according to the number of resources that satisfy the job from different attributes of the resource, such as the speed of the resource, memory and so on. Different kinds have different priorities in the scheduling. For the job that belongs to the same kinds, we propose CPLMT to schedule those jobs. Comparisons between our method, FIFO (First in first out), ASJS (Adaptive Scoring Job Scheduling), Fair and CMMS (Cloud-Minmin) are executed under different environments. The simulation results show that our proposed CPLMT not only reduces the number of unfinished jobs, but also reduces the average execution time.

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References

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