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Granularity-based workflow scheduling algorithm for cloud computing

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Abstract

The workflow scheduling problem has drawn a lot of attention in the research community. This paper presents a workflow scheduling algorithm, called granularity score scheduling (GSS), which is based on the granularity of the tasks in a given workflow. The main objectives of GSS are to minimize the makespan and maximize the average virtual machine utilization. The algorithm consists of three phases, namely B-level calculation, score adjustment and task ranking and scheduling. We simulate the proposed algorithm using various benchmark scientific workflow applications, i.e., Cybershake, Epigenomic, Inspiral and Montage. The simulation results are compared with two well-known existing workflow scheduling algorithms, namely heterogeneous earliest finish time and performance effective task scheduling, which are also applied in cloud computing environment. Based on the simulation results, the proposed algorithm remarkably demonstrates its performance in terms of makespan and average virtual machine utilization.

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Authors and Affiliations

  1. Department of Computer Science and Engineering, Indian Institute of Technology (ISM), Dhanbad, 826004, India

    Madhu Sudan Kumar, Indrajeet Gupta & Prasanta K. Jana

  2. Department of Computer Science and Engineering and Information Technology, Veer Surendra Sai University of Technology, Burla, 768018, India

    Sanjaya K. Panda

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  1. Madhu Sudan Kumar
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  2. Indrajeet Gupta
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  3. Sanjaya K. Panda
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  4. Prasanta K. Jana
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Correspondence to Madhu Sudan Kumar.

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Kumar, M.S., Gupta, I., Panda, S.K. et al. Granularity-based workflow scheduling algorithm for cloud computing. J Supercomput 73, 5440–5464 (2017). https://doi.org/10.1007/s11227-017-2094-7

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  • DOI: https://doi.org/10.1007/s11227-017-2094-7

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