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A Novel Genetic Algorithm for Workflow Scheduling in Cloud Computing Environment


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1 Department of Computer Science and Enginering, Tirunelveli, India
     

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The swarm intelligence is a relatively new approach for problem solving that takes inspiration from the social behaviours of insects and of other animals. In particular, ants have inspired a number of methods and techniques among which the most studied and the most successful is the general purpose optimization technique known as ant colony optimization. The technique for finding the shortest path was applied in cloud computing. The genetic algorithm and ant colony approach [4] [5] [6] and [1] towards cloud computing gives better performance.


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  • Achary, R., Vityanathan, V., Raj, P., and Nagarajan, S. 2015. Dynamic Job Scheduling Using Ant Colony Optimization for Mobile Cloud Computing. In Intelligent Distributed Computing, Springer International Publishing, pp. 71-82.
  • Banerjee, S., Mukherjee, I., and Mahanti, P. K. 2009. Cloud computing initiative using modified ant colony framework. World Academy of Science, Engineering and Technology, 56, 221-224.
  • Hu, X. X., and Zhou, X. W. 2014. Improved Ant Colony Algorithm on Scheduling Optimization of Cloud Computing Resources. In Applied Mechanics and Materials, Vol. 678, pp. 75-78.
  • HUA, X. Y., Zheng, J., and HU, W. X. 2010. Ant colony optimization algorithm for computing resource allocation based on cloud computing environment [J]. Journal of East China Normal University (Natural Science), 1(1), 127-134.
  • Li, K., Xu, G., Zhao, G., Dong, Y., and Wang, D. 2011. Cloud task scheduling based on load balancing ant colony optimization. In Chinagrid Conference (ChinaGrid), 2011 Sixth Annual, IEEE, pp. 3-9.
  • Liu, H., Xu, D., and Miao, H. 2011. Ant colony optimization based service flow scheduling with various QoS requirements in cloud computing. InSoftware and Network Engineering (SSNE), 2011 First ACIS International Symposium, IEEE, pp. 53-58.
  • Mishra, R., and Jaiswal, A. 2012. Ant colony optimization: A solution of load balancing in cloud. International Journal of Web & Semantic Technology (IJWesT), 3(2), 33-50.
  • Nishant, K., Sharma, P., Krishna, V., Gupta, C., Singh, K. P., Nitin, N., and Rastogi, R. 2012. Load balancing of nodes in cloud using ant colony optimization. In Computer Modelling and Simulation (UKSim), 2012 UKSim 14th International Conference, IEEE, pp. 3-8.
  • Yu, Q., Chen, L., and Li, B. 2015. Ant colony optimization applied to web service compositions in cloud computing. Computers & Electrical Engineering, 41, 18-27.
  • Zhulin Li, Z. L., Cuirong Wang, C. W., Haiyan Lv, H. L., and Xin Song, X. S. (2014). Scheduling Tasks on Heterogeneous Multi-Core Processors Based on Modified Ant Colony Optimization. International Journal of Control and Automation, 7(9), 345-356.

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  • A Novel Genetic Algorithm for Workflow Scheduling in Cloud Computing Environment

Abstract Views: 318  |  PDF Views: 0

Authors

B. Kanagalakshmi
Department of Computer Science and Enginering, Tirunelveli, India

Abstract


The swarm intelligence is a relatively new approach for problem solving that takes inspiration from the social behaviours of insects and of other animals. In particular, ants have inspired a number of methods and techniques among which the most studied and the most successful is the general purpose optimization technique known as ant colony optimization. The technique for finding the shortest path was applied in cloud computing. The genetic algorithm and ant colony approach [4] [5] [6] and [1] towards cloud computing gives better performance.


References