Abstract
To enhance the speed of data transfer and remote server working performances, the suggested algorithm accomplishes load balancing in virtual machines by maintaining high availability and avoiding downtime issues when a datacenter experiences heavy traffic. This result has been obtained through minimizing the average response time and datacenter processing time by decently scheduling the requests and balancing the incoming load between the VMs. To achieve so, a combination of two scheduling techniques, Ant Colony Optimization System coupled with Threshold implemented Load Balancer algorithm has been designed. This paper also brings up comparisons among various cloud task scheduling algorithms such as Round-Robin (RR) and Active VM Load Balancer with the proposed technique. All algorithms have been simulated using Cloud Analyst toolkit package. Experimental results showed that the proposed Threshold based ACO system outperformed other stated algorithms.
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References
Gao, Y., Guan, H., Qi, Z., Hou, Y., Liu, L.: A multi-objective ant colony system algorithm for virtual machine placement in cloud computing. J. Comput. Syst. Sci. 79(8), 1230–1242 (2013)
Qiyi, H., Tinglei, H.: An optimistic job scheduling strategy based on QoS for cloud computing. In: Proceedings of the IEEE International Conference on Intelligent Computing and Integrated Systems, Guilin, China, pp. 673–675 (2010)
Hamdaqa, M.: Cloud computing uncovered: a research landscape, pp. 41–85. Elsevier Press (2012). ISBN 0-12-396535-7
Mishra, R., Jaiswal, A.: Ant colony optimization: a solution of load balancing in cloud. Int. J. Web Semant. Technol. (IJWesT) 3(2), 33 (2012). https://doi.org/10.5121/ijwest.2012.3203
Bonabeau, E., Dorigo, M., Theraulaz, G.: Swarm Intelligence: From Natural to Artificial Intelligence. Oxford University Press, New York (1999)
Blum, C.: Ant colony optimization: introduction and recent trends. ALBCOM, LSI, Universitat Politècnica de Catalunya, Jordi Girona 1-3, Campus Nord, 08034 Barcelona, Spain. Accepted 11 October 2005
Zuo, L., Shu, L., Dong, S., Zhu, C., Hara, T.: A multi-objective optimization scheduling method based on the ant colony algorithm in cloud computing. https://doi.org/10.1109/ACCESS.2015.2508940
Zuo, L., Shu, L., Dong, S., Chen, Y., Yan, L.: A multi-objective hybrid cloud resource scheduling method based on deadline and cost constraints. IEEE Access. https://doi.org/10.1109/access.2016.2633288
Stützle, T., Hoos, H.H.: MAX-MIN ant system. Future Gener. Comput. Syst. 16(8), 889–914 (2000)
Di Caro, G., Dorigo, M.: AntNet: distributed stigmergetic control for communications networks. J. Artif. Intell. Res. 9(3), 317–365 (1998)
Liu, X.-F., Zhan, Z.-H., Deng, J.D., Li, Y., Gu, T., Zhang, J.: An energy efficient ant colony system for virtual machine placement in cloud computing. IEEE. https://doi.org/10.1109/tevc.2016.2623803
Dorigo, M., Gambardella, L.M.: Ant colony system: a cooperative learning approach to the traveling salesman problem. IEEE Trans. Evol. Comput. 1(1), 53–66 (1997)
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Banerjee, C., Roy, A., Roy, A., Saha, A., De, A.K. (2019). A Time Efficient Threshold Based Ant Colony System for Cloud Load Balancing. In: Mandal, J., Mukhopadhyay, S., Dutta, P., Dasgupta, K. (eds) Computational Intelligence, Communications, and Business Analytics. CICBA 2018. Communications in Computer and Information Science, vol 1030. Springer, Singapore. https://doi.org/10.1007/978-981-13-8578-0_16
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DOI: https://doi.org/10.1007/978-981-13-8578-0_16
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