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

 
 
 
  • Abstract
  • Keywords
  • References
  • PDF
  • 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.

     


  • Keywords


    Cloud; Scientific workflows; Multi-Objective; Resource Provisioning; Task Scheduling

  • References


      [1] Juan J. Durillo, Radu Prodan,”Multi-Objective workflow shceduling in Amazon EC2”, Cluster Computing, Vol.17, No.2, (2014), pp.169-189, available online: https://doi.org/10.1007/s10586-013-0325-0

      [2] Phyo Thandar Thant, Courtney Powell, Martin Schlueter, and Masaharu Munetomo, “Multiobjective Level-Wise Scientific Workflow Optimization in IaaS Public Cloud Environment,” Scientific Programming, Vol. 2017, Article ID 5342727, 17 pages, 2017. https://doi.org/10.1155/2017/5342727.

      [3] Miao Zhang, Huiqi Li, Li Liu and Rajkumar Buyya, “An adaptive multi-objective evolutionary algorithm for constrained workflow scheduling in Clouds,” Distributed and Parallel Databases, Vol. 36 (2017), pp. 339-368.

      [4] Zhaomeng Zhu, Gongxuan Zhang, Miqing Li, Xiaohui Liu, “Evolutionary Multi-Objective Workflow Scheduling in Cloud.” IEEE Transactions on Parallel and Distributed Systems Vol. 27 (2016), pp. 1344-1357.

      [5] Ji Liu, Esther Pacitti, Patrick Valduriez, Daniel De Oliveira, Marta Mattoso, “Multi-Objective Scheduling of Scientific Workflows in Multisite Clouds”, Future Generation Computer Systems, Elsevier, Vol. 2016, 63, pp.76-95, available online: https://doi.org/10.1016/j.future.2016.04.014.

      [6] Heyang Xu, Bo Yang, Weiwei Qi and Emmanuel Ahene, "A Multi-objective Optimization Approach to Workflow Scheduling in Clouds Considering Fault Recovery," KSII Transactions on Internet and Information Systems,Vol. 10, no. 3, pp. 976-995, 2016. doi: 10.3837/tiis.2016.03.002

      [7] K. Deb, L. Thiele, M. Laumanns and E. Zitzler, "Scalable multi-objective optimization test problems," Evolutionary Computation, 2002. CEC '02. Proceedings of the 2002 Congress on, Honolulu, HI, 2002, pp. 825-830.doi: 10.1109/CEC.2002.1007032

      [8] Web reference: https://pythonhosted.org/inspyred/reference.html

      [9] Web reference: http://www.moeaframework.org/


 

View

Download

Article ID: 20457
 
DOI: 10.14419/ijet.v7i4.6.20457




Copyright © 2012-2015 Science Publishing Corporation Inc. All rights reserved.