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Chicken Swarm Optimization in Task Scheduling in Cloud Computing

Volume 15, Number 7, July 2019, pp. 1929-1938
DOI: 10.23940/ijpe.19.07.p20.19291938

Liru Han

Zhejiang University of Water Resources and Electric Power, Hangzhou, 310018, China

 

(Submitted on March 12, 2019; Revised on May 15, 2019; Accepted on June 15, 2019)

Abstract:

In order to solve the problem of low efficiency in resource scheduling in cloud computing, an improved chicken swarm optimization (CSO) is proposed for task scheduling. Firstly, the concept of opposition-based learning is introduced to initialize the chicken population and improve the global search ability. Secondly, the concepts of the weight value and learning factor in particle swarm optimization (PSO) are introduced to improve the positions of chickens, and the individual positions of chickens are optimized. Thirdly, the overall individual positions of the CSO are optimized by the difference algorithm. Finally, the possible cross-boundary of individual positions in the algorithm is prevented as a whole by boundary processing. In the simulation experiment, the optimized CSO is compared with the basic CSO, PSO, and ant colony optimization (ACO) in terms of completion time, cost, energy consumption, and load balancing, and good results are achieved.

 

References: 19

  1. M. Armbrust, A. Fox, R. Griffith, A. D. Joseph, R. Katz, D. A. Patterson, et al., “A View of Cloud Computing,” Communications of the ACM, Vol. 53, No. 4, pp. 50-58, 2010
  2. S. Shahdi-Pashaki, E. Trymourin, and R. Tavakkolmoghaddam, “New Approach based on Group Technology for the Consolidation Problem in Cloud Computing-Mathematical Model and Genetic Algorithm,” Computational and Applied Mathematics, Vol. 37, No. 1, pp. 693-718, 2018
  3. D. Kéo, A. Subasi, and J. Kevric, “Cloud Computing-based Parallel Genetic Algorithm for Gene Selection in Cancer Classification,” Neural Computing and Application, Vol. 30, No. 5, pp. 1601-1610, 2018
  4. M. Masdari, F. Salehi, M. Jalali, and M. Bidaki, “A Survey of PSO-based Scheduling Algorithms in Cloud Computing,” Journal of Network and Systems Management, Vol. 25, No. 1, pp. 122-158, 2017
  5. N. Kumar and P. Patel, “Resource Management using ANN-PSO Techniques in Cloud Environment,” in Proceedings of the 2016 International Congresson Information and Communication Technology, pp. 419-428, 2016
  6. V. S. Kushwah and S. K. Goyal, “A Basic Simulation of ACO Algorithm under Cloud Computing for Fault Tolerant,” in Proceedings of the International Conference on Data Engineering and Communication Technology, pp. 465-472, 2017
  7. A. Ragmani, A. E. Omri, N. Abghour, K. Moussaid, and M. Rida, “A Performed Load Balancing Algorithm for Public Cloud Computing using Ant Colony Optimization,” Recent Patents on Computer Science, Vol. 11, No. 3, pp. 221-228, 2018
  8. F. Kong and D. H. Wu, “An Improved Chicken Swarm Optimization Algorithm,” Journal of Southern Yangtze University (Natural Science Edition), Vol. 14, No. 6, pp. 681-688, 2015
  9. H. M. Hu, J. Y. Li, and J. G. Huang, “Economic Operation Optimization of Micro-Grid based on Chicken Swarm Optimization Algorithm,” High Voltage Apparatus, Vol. 53, No. 1, pp. 119-125, 2017
  10. S. P. Xu, D. H. Wu, and F. Kong, “Solving Flexible Job-Shop Scheduling Problem by Improved Chicken Swarm Optimization Algorithm,” Journal of System Simulation, Vol. 29, No. 7, pp. 1497-1505, 2017
  11. D. H. Wu and S. P. Xu, “Solving Multi-Objective Flexible Job Shop Scheduling Problem by the Chicken Swarm Optimization Algorithm based on Pareto Entropy,” Mini-Micro Systems, Vol. 38, No. 12, pp. 2683-2688, 2017
  12. D. Moldovan, V. R. Chifu, C. B. Pop, T. Cioara, I. Anghel, and I. Salomie, “Chicken Swarm Optimization and Deep Learning for Manufacturing Processes,” in Proceedings of 2018 17th RoEduNet Conference on Networking in Education and Research, pp. 1-6, 2018
  13. J. Grobler and A. P. Engelbrecht, “Arithmetic and Parent-Centric Headless Chicken Crossover Operators for Dynamic Particle Swarm Optimization Algorithms,” Soft Computing, Vol. 22, No. 18, pp. 5965-5976, 2018
  14. S. Torabi and F. Safi-Esfahani, “A Hybrid Algorithm based on Chicken Swarm and Improved Raven Roosting Optimization,” Soft Computing, pp. 1-43, 2018
  15. L. Cheng, I. Tachmazidis, S. Kotoulas, and G. Antoniou, “Design and Evaluation of Small-Large Outer Joins in Cloud Computing Environments,” Journal of Parallel and Distributed Computing, Vol. 110, pp. 2-15, 2017
  16. D. H. Wu, F. Kong, and Z. C. Ji, “Convergence Analysis of Chicken Swarm Optimization Algorithm,” Journal of Central South University (Science and Technology), Vol. 48, No. 8, pp. 2105-2112, 2017
  17. H. R. Tizhoosh, “Opposition-based Learning: A New Scheme for Machine Intelligence,” in Proceedings of the 2005 International Conference on Computational Intelligence for Modelling, Control and Automation and International Conference on Intelligent Agents, Web Technologies and Internet Commerce, pp. 695-701, 2005
  18. H. Wang, Z. J. Wu, S. Rahnamayan, Y. Liu, and M. Ventresca, “Enhancing Particle Swarm Optimization using Generalized Opposition-based Learning,” Information Sciences, Vol. 181, No. 20, pp. 4699-4714, 2011
  19. Y. M. Bai, “Particle Swarm Optimization and Its Application,” LanZhou Jiaotong University, pp. 8-9, 2013

 

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