A workflow scheduling technique using genetic algorithm in spot instance-based cloud

Daeyong Jung, Taeweon Suh, Heonchang Yu, Joonmin Gil

Research output: Contribution to journalArticlepeer-review

15 Citations (Scopus)


Cloud computing is a computing paradigm in which users can rent computing resources from service providers according to their requirements. A spot instance in cloud computing helps a user to obtain resources at a lower cost. However, a crucial weakness of spot instances is that the resources can be unreliable anytime due to the fluctuation of instance prices, resulting in increasing the failure time of users’ job. In this paper, we propose a Genetic Algorithm (GA)-based workflow scheduling scheme that can find the optimal task size of each instance in a spot instance-based cloud computing environment without increasing users’ budgets. Our scheme reduces total task execution time even if an out-of-bid situation occurs in an instance. The simulation results, based on a before-and-after GA comparison, reveal that our scheme achieves performance improvements in terms of reducing the task execution time on average by 7.06%. Additionally, the cost in our scheme is similar to that when GA is not applied. Therefore, our scheme can achieve better performance than the existing scheme, by optimizing the task size allocated to each available instance throughout the evolutionary process of GA.

Original languageEnglish
Pages (from-to)3126-3145
Number of pages20
JournalKSII Transactions on Internet and Information Systems
Issue number9
Publication statusPublished - 2014 Sept 30


  • Cloud computing
  • Fault tolerance
  • Genetic algorithm
  • Price history
  • Spot instances
  • Workflow

ASJC Scopus subject areas

  • Information Systems
  • Computer Networks and Communications


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