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Optimization of Particle Genetic Algorithm based on Time Load Balancing for Cloud Task Scheduling in Cloud Task Planning

Volume 14, Number 6, June 2018, pp. 1161-1170
DOI: 10.23940/ijpe.18.06.p7.11611170

Yenzhen Zhang, Shouming Hou, and Li Chang

School of Computer Science and Technology, Henan Polytechnic University, Jiaozuo, 454000, China

(Submitted on March 7, 2018; Revised on April 29, 2018; Accepted on May 24, 2018)


To solve the problems of long time consumption, imbalanced time load and low resource utilization for cloud task scheduling in cloud task planning, we propose an optimized strategy of particle genetic algorithm based on time load balancing. This strategy was adopted to improve the quality of particles by optimizing particle initialization operation. To ensure that better particles capable of more balanced time load are selected, a model of fitness in time load balancing was established. To prevent the particles from jumping out of the specified area in iterations, the element values of their location and velocity were processed in a standardized way. Finally, genetic crossover and mutation operators were introduced to avoid leading the algorithm to local optimization. This strategy could effectively improve the convergence rate of the particle genetic algorithm and the quality of solutions. The experimental results showed that the algorithm had greater power to search for a better global optimal solution, consumed less time, and reached a more balanced time load. With this algorithm, we may achieve better and more logical task scheduling sequences. Simultaneously, the idea owns a certain degree of practicality and generalization in many fields.


References: 25

        1. M. Alouane, and H. E. Bakkali,Virtualization in Cloud Computing: Existing Solutions and New Approach,” International Conference on Cloud Computing Technologies and Applications. IEEE, pp. 116-123, 2017.
        2. R. Buyya, C. S. Yeo, S. Venugopal, J. Brobery, and I. Brandic, “Cloud Computing and Emerging IT Platforms: Vision, Hype, and Reality for Delivering Computing as the 5th Utility,” Future Generation Computer Systemss, vol. 25, no. 6, pp. 599-616, 2009.
        3. K. Chadha, and A. Bajpai, “Security Aspects of Cloud Computing,” International Journal of Computer Applications, vol. 40, no. 8, pp. 43-47, 2012.
        4. Q. Cai, D. Shan, and W. Zhao, “Resource Scheduling in Cloud Computer based on Improved Particle Swarm Optimization Algorithm,” Journal of Liaoning Technical University, 2016.
        5. H. Chang, and X. Tang, “A Load-balance based Resource-scheduling Algorithm under Cloud Computing Environment,” International Conference on Web-Based Learning. Springer, Berlin, Heidelberg, pp. 85-90, 2010.
        6. Y. U. Guo-Long, Z. W. Cui, and Y. Zuo, “Cloud Platform Scheduling Method based on Optimized Particle Swarm Optimization Algorithm,” Journal of Inner Mongolia Normal University, 2016.
        7. H. Grillo, D. Peidro, M. M. E. Alemany, and J. Mula, “Application of Particle Swarm Optimisation with Backward Calculation to Solve a Fuzzy Multi-objective Supply Chain Master Planning Model,” International Journal of Bio-Inspired Computation, vol. 7, no. 3, pp. 157-169, 2015.
        8. G. Jung, and K. M. Sim, “Agent-based Adaptive Resource Allocation on the Cloud Computing Environment,” International Conference on Parallel Processing Workshops IEEE, pp. 345-351, 2011.
        9. N. J. Kansal, and I. Chana, “Cloud Load Balancing Techniques: A StepTowards Green Computing,” IJCSI International Journal of Computer Science Issues, vol. 9, no. 1, pp. 238-246, 2012.
        10. G. Kumughato, and J. Priya, “A Survey of Load Balancing Techniques in Cloud Environment,” International Journal of Advanced Research in Computer Science, vol. 5, no. 1, 2014.
        11. X. L. Li, “Research on Cloud Computing Task Scheduling based on Simulated Annealing Genetic Algorithm,” Huazhong Normal University, 2016.
        12. N. D. Lockin, and Acknowledgments, “Journal of Cloud Computing,” Communications of the ACM, vol. 4, no. 2, pp. 50-58, 2013.
        13. H. Mehta, P. Kanungo, and M. Chandwani, “Decentralized Content Aware Load Balancing Algorithm for Distributed Computing Environments,” Proceedings of the International Conference & Workshop on Emerging Trends in Technology. ACM, pp. 370-375, 2011.
        14. A. M. Nakai, E. Madeira, and L. E. Buzato, “Load Balancing for Internet Distributed Services Using Limited Redirection Rates,” Dependable Computing (LADC), 2011 5th Latin-American Symposium on. IEEE, pp. 156-165, 2011.
        15. P. Rajanna, and J. Gyani, “A Comparative Study of Cloud and Grid Computing Security Solutions,” International Journal of Computer Science and Electronics Engineering, vol. 2, no. 1, pp. 1-8, 2012.
        16. M. Randles, D. Lamb, and A. Taleb-Bendiab, “A Comparative Study into Distributed Load Balancing Algorithms for Cloud Computing,” Advanced Information Networking and Applications Workshops (WAINA), 2010 IEEE 24th International Conference on. IEEE, pp. 551-556, 2010.
        17. M. A. Sharkh, A. Ouda, and A. Shami, “A Resource Scheduling Model for Cloud Computing Data Centers,” Wireless Communications and Mobile Computing Conference (IWCMC), 2013 9th International. IEEE, pp. 213-218, 2013.
        18. S. Selvarani, and G. S. Sadhasivam, “Improved Cost-based Algorithm for Task Scheduling in Cloud Computing,” IEEE International Conference on Computational Intelligence and Computing Research. IEEE, pp. 1-5, 2011.
        19. Y. Wang, Y. Sun, and Y. Sun, “Task Scheduling Algorithm in Cloud Computing based on Fairness Load Balance and Minimum Completion Time,” Advances in Engineering Research, 2016.
        20. S. C. Wang, K. Q. Yan, W. P. Liao, and S. S. Wang, “Towards a Load Balancing in a Three-level Cloud Computing Network,” Computer Science and Information Technology (ICCSIT), 2010 3rd IEEE International Conference on. IEEE, pp. 1: 108-113, 2010.
        21. X. H. Wang, D. S. Zou, “An Cloud Computing Task Scheduling Method based on Particle Swarm Optimization Genetic Algorithm (PSO-GA),” World Science and Technology Research and Development, no. 02, pp. 110-114, 2014.
        22. J. Xu, and Y. Tang, “Research of Improved Particle Swarm Optimization based on Genetic Algorithm for Hadoop Task Scheduling Problem,” vol. 2, pp. 60-66, 2015.
        23. Y. Zhao, and W. Huang, “Adaptive Distributed Load Balancing Algorithm based on Live Migration of Virtual Machines in Cloud,” INC, IMS and IDC, 2009. NCM'09. Fifth International Joint Conference on. IEEE, pp. 170-175, 2009.
        24. J. Zhu, and D. Xiao, “Multi-dimensional QoS Constrained Task Scheduling Mechanism for Load Balancing Under Cloud Computing,” Computer Engineering and Applications, vol. 49, no. 9, pp. 85-89, 2013.
        25. Z. Zhang, and X. Zhang, “A Load Balancing Mechanism Based on Ant Colony and Complex Network Theory in Open Cloud Computing Federation,” Industrial Mechatronics and Automation (ICIMA), 2010 2nd International Conference on. IEEE, vol. 2, pp. 240-243, 2010.


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