Bi-objective decision support system for task-scheduling based on genetic algorithm in cloud computing-Reference-Cited by-同舟云学术

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Bi-objective decision support system for task-scheduling based on genetic algorithm in cloud computing

Published:2017-07-20 Issue:2 Volume:100 Page:65-91
ISSN:0010-485X
Container-title:Computing
language:en
Short-container-title:Computing

Author:

Aziza Hatem^ORCID,Krichen Saoussen

Publisher

Springer Science and Business Media LLC

Subject

Computational Mathematics,Computational Theory and Mathematics,Computer Science Applications,Numerical Analysis,Theoretical Computer Science,Software

Link

http://link.springer.com/article/10.1007/s00607-017-0566-5/fulltext.html

Reference30 articles.

1. Agarwal A, Jain S (2014) Efficient optimal algorithm of task-scheduling in cloud computing environment. Int J Comput Trends Technol 9(7):344–349

2. Alkhanak EN, Lee SP, Reza R, Parizi RM (2015) Cost optimization approaches for scientific workflow scheduling in cloud and grid computing: a review, classifications, and open issues. J Syst Softw. doi: 10.1016/j.jss.2015.11.023

3. Berwal M, Kant C (2015) Load balancing in cloud computing using task-scheduling. Int J Adv Res Comput Commun Eng. doi: 10.17148/IJARCCE.2015.4737

4. Calheiros RN, Ranjan R, Beloglazov A, De Rose CAF, Buyya R (2011) CloudSim: a toolkit for modeling and simulation of cloud computing environments and evaluation of resource provisioning algorithms. Softw Pract Exper 41:23–50. doi: 10.1002/spe.995

5. Downey Allen B (1996) Predicting queue times on space-sharing parallel computers. University of California at Berkeley, Berkeley

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