An Efficient Algorithm for Workflow Scheduling in the Clouds Based on Differential Evolution Method
American Journal of Computer Science and Technology
Volume 1, Issue 1, March 2018, Pages: 24-30
Received: Oct. 27, 2017; Accepted: Dec. 4, 2017; Published: Jan. 2, 2018
Views 1030      Downloads 89
Authors
Toan Phan Thanh, Faculty of Technology Education, Hanoi National University of Education, Ha Noi, Viet Nam
Loc Nguyen The, Faculty of Information Technology, Hanoi National University of Education, Ha Noi, Viet Nam
Said Elnaffar, School of Engineering, Computer Science Department, American University of RAK, Ras al Khaimah, UAE
Article Tools
Follow on us
Abstract
The Cloud is a computing platform that provides on-demand access to a shared pool of configurable resources such as networks, servers, storage that can be rapidly provisioned and released with minimal management effort from clients. At its core, Cloud computing focuses on manimizing the effectiveness of the shared resources. Therefore, workflow scheduling is one of the challenges that the Cloud must tackle especially if a large number of tasks are executed on geographically distributed servers. The Cloud is comprised of computational and storage servers that aim to provision efficient access to remote and geographically distributed resources. To that end, many challenges, specifically workflow scheduling, are yet to be solved such. Despite it has been the focus of many researchers, a handful efficient solutions have been proposed for Cloud computing. In this work, we propose a novel algorithm for workflow scheduling that is derived from the Opposition-based Differential Evolution method, MODE. This algorithm not only ensures fast convergence but also averts getting trapped in local extrema. Our simulation experiments Cloud Sim show that MODE is superior to its predecessors. Moreover, the deviation of its solution from the optimal one is negligible.
Keywords
Workflow Scheduling, Opposition-Based Differential Evolution, Cloud Computing, Differential Evolution
To cite this article
Toan Phan Thanh, Loc Nguyen The, Said Elnaffar, An Efficient Algorithm for Workflow Scheduling in the Clouds Based on Differential Evolution Method, American Journal of Computer Science and Technology. Vol. 1, No. 1, 2018, pp. 24-30. doi: 10.11648/j.ajcst.20180101.14
Copyright
Copyright © 2018 Authors retain the copyright of this article.
This article is an open access article distributed under the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/) which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
References
[1]
R. N. Calheiros, R. Ranjan, A. Beloglazov, Cesar A. F. De Rose, and R. Buyya, CloudSim: A Toolkit for Modeling and Simulation of Cloud Computing Environments and Evaluation of Resource Provisioning Algorithms, Software: Practice and Experience, volume 41, Number 1, Pages: 23-50, Wiley Press, USA, 2011
[2]
J. V. Vliet, F. Paganelli, Programming Amazon EC2, O'Reilly Media, ISBN 1449393683, 2011
[3]
http://montage.ipac.caltech.edu
[4]
J. D. Ullman, NP-complete scheduling problems, Journal of Computer and System Sciences, pages 384-393, volume 10, issue 3, 1975
[5]
S. Parsa, R. E. Maleki, RASA: A New Task Scheduling Algorithm in Grid Environment, International Journal of Digital Content Technology and its Applications, volume 3, No. 4, 2009
[6]
A. Agarwal, S. Jain, Efficient Optimal Algorithm of Task Scheduling in Cloud Computing Environment, International Journal of Computer Trends and Technology Volume 9, 2014
[7]
J. Huang, The Workflow Task Scheduling Algorithm Based on the GA Model in the Cloud Computing Environment, Journal of software, volume 9, 2014
[8]
H. Liu, A. Abraham, C. Grosan, A Novel Variable Neighborhood Particle Swarm Optimization for Multi-objective Flexible Job-Shop Scheduling Problems, Proc. of 2nd International Conference on Digital Information Management (ICDIM '07), Volume 1, pages 138-145, 2007.
[9]
S. Pandey, L. Wu1, S. M. Guru, R. Buyya1, A Particle Swarm Optimization (PSO)-based Heuristic for Scheduling Workflow Applications in Cloud Computing Environments, Proc. of 24th IEEE International Conference on Advanced Information Networking and Applications (AINA), pages 400-407, 2010
[10]
J. Kennedy, R. C. Eberhart, Particle swarm optimization, Proc. of IEEE International Conference on Neural Networks. pages 1942–1948, 1995
[11]
A. E. M. Zavala, EVOLVE - A Bridge between Probability, Set Oriented Numerics, and Evolutionary Computation IIA Comparison, A Comparison Study of PSO Neighborhoods, pages 251-295, Springer-Verlag Berlin Heideberg, 2013
[12]
M. Mitzenmacher, E. Upfal, Probability and Computing: Randomized Algorithms and Probabilistic Analysis, Cambridge University Press (2005)
[13]
Dr. Sudha Sadhasivam, R. Jayarani, Dr. N. Nagaveni, R. Vasanth Ram, Design and Implementation of an efficient Twolevel Scheduler for Cloud Computing Environment, In Proceedings of International Conference on Advances in Recent Technologies in Communication and Computing, 2009
[14]
R. Buyya, R. Calheiros, Modeling and Simulation of Scalable Cloud Environment and the Cloud Sim Toolkit: Challenges and Opportunities, IEEE publication 2009, pp1-11
[15]
G. Guo-Ning and H. Ting-Lei, Genetic Simulated Annealing Algorithm for Task Scheduling based on Cloud Computing Environment, In Proceedings of International Conference on Intelligent Computing and Integrated Systems, 2010, pp. 60-63
[16]
L. Guo, Task Scheduling Optimization in Cloud Computing Based on Heuristic Algorithm, Journal of networks, v.7, No.3, 2012, pp. 547-552
[17]
R. Rajkumar, T. Mala, Achieving Service Level Agreement in Cloud Environment using Job Prioritization in Hierarchical Scheduling, Proceeding of International Conference on Information System Design and Intelligent Application, 2012, vol 132, pp 547-554
[18]
Q. Cao, W. Gong and Z. Wei, An Optimized Algorithm for Task Scheduling Based On Activity Based Costing in Cloud Computing, In Proceedings of Third International Conference on Bioinformatics and Biomedical Engineering, 2009, pp.1-3
[19]
R. Storn and K. Price, Differential Evolution-A Simple and Efficient Heuristic for Global Optimization over Continuous Spaces, Journal of Global Optimization, 1997, pp. 341-359
[20]
H. R. Tizhoosh, Opposition-based learning: A new scheme for machine intelligence, International Conference on Computational Intelligence for Modelling, 2005, pp. 695-701
ADDRESS
Science Publishing Group
1 Rockefeller Plaza,
10th and 11th Floors,
New York, NY 10020
U.S.A.
Tel: (001)347-983-5186