Multi-Objective Optimization for scientific workflow task scheduling in IaaS Cloud

  • Authors

    • Arunkumar Panneerselvam
    • Bhuvaneswari Subbaraman
    2018-09-25
    https://doi.org/10.14419/ijet.v7i4.6.20457
  • Cloud, Scientific workflows, Multi-Objective, Resource Provisioning, Task Scheduling

  • Abstract

    The use of scientific applications on cloud networks increases day by day generating volumes of data and consuming large computational power. These scientific applications find its importance in the field of astronomy, geology, genetics and bio-technology etc. Complex and mission critical scientific applications can be modeled as scientific workflows and can be executed in cloud. The tasks of the scientific applications are generally data intensive and compute intensive. Traditional computer networks are not suitable for handling scientific applications and hence ubiquitous distributed networks like cloud are prominent in hosting scientific applications. The cloud hosted scientific applications and the cloud network need to satisfy many objectives to the interest of its users. This paper explores the    multi-objective optimization applications in scientific workflow task scheduling in IaaS cloud and the related algorithms employed.

     

  • References

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  • How to Cite

    Panneerselvam, A., & Subbaraman, B. (2018). Multi-Objective Optimization for scientific workflow task scheduling in IaaS Cloud. International Journal of Engineering & Technology, 7(4.6), 174-176. https://doi.org/10.14419/ijet.v7i4.6.20457

    Received date: 2018-09-29

    Accepted date: 2018-09-29

    Published date: 2018-09-25