sis 20(24): e7

Research Article

Workflow scheduling and reliability improvement by hybrid intelligence optimization approach with task ranking

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  • @ARTICLE{10.4108/eai.13-7-2018.161408,
        author={S.  Khurana and R. K.  Singh},
        title={Workflow scheduling and reliability improvement by hybrid intelligence optimization approach with task ranking},
        journal={EAI Endorsed Transactions on Scalable Information Systems},
        volume={7},
        number={24},
        publisher={EAI},
        journal_a={SIS},
        year={2019},
        month={11},
        keywords={Workflow Scheduling, Reliability, Cloud, Virtualization, Hybrid Optimization},
        doi={10.4108/eai.13-7-2018.161408}
    }
    
  • S. Khurana
    R. K. Singh
    Year: 2019
    Workflow scheduling and reliability improvement by hybrid intelligence optimization approach with task ranking
    SIS
    EAI
    DOI: 10.4108/eai.13-7-2018.161408
S. Khurana1,*, R. K. Singh2
  • 1: I. K. Gujral Punjab Technical University, Jalandhar, Punjab, India
  • 2: SUS Institute of Computer, Tangori, Mohali, Punjab, India
*Contact email: savu.khurana30@gmail.com

Abstract

Workflow scheduling is one of the most challenging tasks in cloud computing. It uses different workflows and quality of service requirements based on the deadline and cost of the tasks. The main goal of workflow scheduling algorithm is to optimize the time and cost by using virtual machine migration. This algorithm computes the subset problem and decision problem in NP time. It works on the decision-making process to reduce the time and cost of computation on the server side. This paper proposes hybrid optimization to optimize the virtual machine locally and globally. The PEFT algorithm is used for initialization and worked as a heuristic algorithm. This algorithm reduces the error of random initialization of optimization. The proposed algorithm based upon Flower pollination with Grey Wolf Optimization using hybrid approach shows significant end effective results than flower pollination with genetic algorithm. The proposed approach also considered the reliability parameter on different workflows.