Dynamic job scheduling method based on expected probability of completion of voting in volunteer computing

Yuto Miyakoshi, Shinya Yasuda, Kan Watanabe, Masaru Fukushi, Yasuyuki Nogami

Research output: Contribution to journalArticlepeer-review

4 Citations (Scopus)


This paper addresses the problem of job scheduling in volunteer computing (VC) systems where each computation job is replicated and allocated to multiple participants (workers) to remove incorrect results by a votingmechanism. In the job scheduling of VC, the number of workers to complete a job is an important factor for the system performance; however, it cannot be fixed because some of the workers may secede in real VC. This is the problem that existing methods have not considered in the job scheduling. We propose a dynamic job scheduling method which considers the expected probability of completion (EPC) for each job based on the probability of worker's secession. The key idea of the proposed method is to allocate jobs so that EPC is always greater than a specified value (SPC). By setting SPC as a reasonable value, the proposed method enables to complete jobs without excess allocation, which leads to the higher performance of VC systems. We assume in this paper that worker's secession probability follows Weibull-distribution which is known to reflect more practical situation. We derive parameters for the distribution using actual trace data and compare the performance of the proposed and the previous method under the Weibull-distribution model, as well as the previous constant probability model. Simulation results show that the performance of the proposed method is up to 5 times higher than that of the existing method especially when the time for completing jobs is restricted, while keeping the error rate lower than a required value.

Original languageEnglish
Pages (from-to)2132-2140
Number of pages9
JournalIEICE Transactions on Information and Systems
Issue number12
Publication statusPublished - Dec 2015


  • Desktop grids
  • Parallel computing
  • Probabilistic method
  • Sabotage-tolerance

ASJC Scopus subject areas

  • Software
  • Hardware and Architecture
  • Computer Vision and Pattern Recognition
  • Electrical and Electronic Engineering
  • Artificial Intelligence


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