Hybrid Algorithm Based on Swarm Intelligence Techniques for Dynamic Tasks Scheduling in Cloud Computing

Автор: Medhat A. Tawfeek, Gamal F. Elhady

Журнал: International Journal of Intelligent Systems and Applications(IJISA) @ijisa

Статья в выпуске: 11 vol.8, 2016 года.

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Cloud computing has its characteristics along with some important issues that should be handled to improve the performance and increase the efficiency of the cloud platform. These issues are related to resources management, fault tolerance, and security. The purpose of this research is to handle the resource management problem, which is to allocate and schedule virtual machines of cloud computing in a way that help providers to reduce makespan time of tasks. In this paper, a hybrid algorithm for dynamic tasks scheduling over cloud's virtual machines is introduced. This hybrid algorithm merges the behaviors of three effective techniques from the swarm intelligence techniques that are used to find a near optimal solution to difficult combinatorial problems. It exploits the advantages of ant colony behavior, the behavior of particle swarm and honeybee foraging behavior. Experimental results reinforce the strength of the proposed hybrid algorithm. They also prove that the proposed hybrid algorithm is the best and outperformed ant colony optimization, particle swarm optimization, artificial bee colony and other known algorithms.

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Cloud Computing, Task Scheduling, Ant Colony Optimization, Particle Swarm Optimization, Artificial Bee Colony, Makespan, CloudSim

Короткий адрес: https://sciup.org/15010876

IDR: 15010876

Список литературы Hybrid Algorithm Based on Swarm Intelligence Techniques for Dynamic Tasks Scheduling in Cloud Computing

  • R. Buyya, J. Broberg, and A. M. Goscinski, "Cloud Computing Principles and Paradigms", Wiley Publishing, 2011.
  • A. N. Toosi, R. N. Calheiros, and R. Buyya, "Interconnected Cloud Computing Environments: Challenges, Taxonomy, and Survey", ACM Comput. Surv., vol. 47, no. 1, pp. 1-7, may 2014
  • A. O. Akande, N. A. April, and J. V. Belle, "Management Issues with Cloud Computing", in Proceedings of the Second International Conference on Innovative Computing and Cloud Computing, New York, USA, pp. 119-124, 2013.
  • E. Pacini, C. Mateos, and C. G. Garino, "Distributed job scheduling based on Swarm Intelligence: A survey", Computers & Electrical Engineering, vol. 40, no. 1, pp. 252-269, 2014.
  • P. Singhal, S. K. Agarwal, N. Kumar "Advanced Adaptive Particle Swarm Optimization based SVC Controller for Power System Stability" IJISA Vol. 7, No. 1, PP.101-110, December 2014.
  • G. B, F. Zohra, T, and Wieme, Z. "Approaches to Improve the Resources Management in the Simulator CloudSim" in ICICA 2010, LNCS 6377, pp. 189–196, 2010.
  • M. A. Tawfeek, A. El-Sisi, A. E. keshk and F. A. Torkey, "Cloud Task Scheduling Based on Ant Colony Optimization", International Arab Journal of Information Technology (IAJIT), vol. 12, no. 2, pp. 129-137,2015.
  • A. El-Sisi, M. A. Tawfeek, A. E. keshk and F. A. Torkey, "Intelligent Method for Cloud Task Scheduling Based on Particle Swarm Optimization Algorithm", The International Arab Conference on Information Technology(ACIT), Oman, 2014.
  • M. A. Tawfeek, A. El-Sisi, A. E. keshk and F. A. Torkey, " Artificial Bee Colony Algorithm for Cloud Task Scheduling", International Journal of Computer and Information (IJCI), vol. 4, no. 1, pp. 1-9, 2015.
  • R. F. T. Neto and M. G. Filho, "Literature Review regarding Ant Colony Optimization Applied to Scheduling Problems: Guidelines for Implementation and Directions for Future Research", Engineering Applications of Artificial Intelligence, vol. 26, no. 1, pp. 150-161, 2013.
  • E. Talbi, "Metaheuristics from Design to Implementation", Hoboken, New Jersey: John Wiley & Sons, Inc., 2009.
  • S. V. Kamble, S. U. Mane, A. J. Umbarkar "Hybrid Multi-Objective Particle Swarm Optimization for Flexible Job Shop Scheduling Problem" IJISA Vol. 7, No. 4, PP.54-61, March 2015
  • B. Soumya, M. Indrajit, and P. Mahanti, "Cloud Computing Initiative using Modified Ant Colony Framework," In the World Academy of Science, Engineering and Technology, vol. 56, pp. 221-224, 2009.
  • M. A. Tawfeek, A. El-Sisi, A. E. keshk and F. A. Torkey, "An Ant Algorithm for Cloud Task Scheduling", International Workshop on Cloud Computing and Information Security CCIS, pp. 169-172, China, 2013.
  • S. Thomas and H. H. Holger, "MAX-MIN Ant System", Future Generation Computer Systems, vol. 16, no. 8, pp. 889-914, 2000.
  • K. and Sharma, P. and Krishna, V. and Gupta, C. and Singh, K.P. and Nitin, N. and Rastogi, R. Nishant, "Load Balancing of Nodes in Cloud Using Ant Colony Optimization", in Computer Modelling and Simulation (UKSim), pp. 3-8, 2012.
  • K. Li, G. Xu, G. Zhao, Y. Dong, and D. Wang, "Cloud Task Scheduling Based on Load Balancing Ant Colony Optimization", in Chinagrid Conference (ChinaGrid), pp. 3-9, Aug 2011.
  • A. E. keshk, A. El-Sisi, M. A. Tawfeek, F. A. Torkey, "Intelligent Strategy of Task Scheduling in Cloud Computing for Load Balancing", International Journal of Emerging Trends & Technology in Computer Science (IJETTCS), vol. 2, no. 6, pp.12-22,2013.
  • S. Pandey, Linlin Wu, S. M. Guru, and R. Buyya, "A Particle Swarm Optimization-Based Heuristic for Scheduling Workflow Applications in Cloud Computing Environments", in 24th IEEE International Conference on Advanced Information Networking and Applications (AINA) , pp. 400-407, April 2010.
  • D. Babu L.D. and P. Venkata Krishna, "Honey Bee Behavior Inspired Load Balancing of Tasks in Cloud Computing Environments," Applied Soft Computing, vol. 13, no. 5, pp. 2292-2303, 2013
  • Y. Lua et al., "Join-Idle-Queue: A Novel Load Balancing Algorithm for Dynamically Scalable Web Services," International Journal of Performance Evaluation, August 2011.
  • V. Gupta, M. Harchol-Balter, K. Sigman, and W. Whitt, "Analysis of Join-the-shortest-queue Routing for Web Server Farms," Perforance Evaluation, vol. 64, pp. 1062–1081, 2011.
  • B. Mondal, K. Dasgupta, and P. Dutta, "Load Balancing in Cloud Computing using Stochastic Hill Climbing A Soft Computing Approach," Procedia Technology , vol. 4, pp. 783–789, 2012.
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