Abstract
Load balancing over the cloud environment for computing is not a new problem. However, balancing the loads in proper and efficient way to maximize resource utilization is an issue or a problem. This paper focuses that how to balance the loads and use the resources in maximum utilization using CloudSim tool. The average number of cloudlets and the total cost are the key parameters those are used to interpret and analyze the certain results. While loading the balance, these parameters are distinguished cost and failing ratio of the obtained results. The results are used to take care of enhancing the proper resource utilization using ACO algorithm. An ACO is a better approach to provide the higher great ability in terms of usage of virtual machine, bandwidth, number of clouds, memory, etc. The work can be carried out by the improving designing new modified ACO and minimum execution time.
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References
Chen, H., Wang, F., Helian, N., Akanmu, G.: User-priority guided min-min scheduling algorithm for load balancing in cloud computing. In: IEEE International Conference on Parallel Computing Technologies, pp. 1–8 (2013)
Patel, B., Patel, S.: Various load balancing algorithms in cloud computing. IJARIIE-ISSN (O)-2395-4396, 1(2), 187–202 (2015)
Al-Sharaa, B., Al-Qublan, T.: Bounded ant colony algorithm fortask allocation on a network of homogeneous processors using a primary site (bts-aco). Int. J. Comput. Sci. Inf. Technol. 5(3), 165 (2013)
Banerjee, S., Mukherjee, I., Mahanti, PK.: Cloud computing initiative using modified ant colony framework. World Acad. Sci. Eng. Technol. 56, 221–224 (2009)
Lu, X., Gu, Z.: A load-adapative cloud resource scheduling model based on ant colony algorithm. In: IEEE (2011)
Li, K., Xu, G., Zhao, G., Dong, Y., Wang, D.: Cloud task scheduling based on load balancing ant colony optimization. In: IEEE (2011)
Song, X., Gao, L., Wang, J.: Job scheduling based on ant colony optimization in cloud computing. In: IEEE (2011)
Bo, Z., Ji, G., Jieqing, A.: Cloud loading balance algorithm. In: IEEE (2011)
Arora, V., Tyagi, S.S.: Performance evaluation of load balancing policies across virtual machines in a data center. In: IEEE International Conference on Reliability, Optimization and Information Technology—ICROIT, pp. 384–387 (2014)
Pilavare, M.S., Desai, A.: A novel approach towards improving performance of load balancing using genetic algorithm in cloud computing. In: IEEE Sponsored 2nd International Conference on Innovations in Information Embedded and Communication Systems ICIIECS (2015)
Nagpure, M.B., Dahiwale, P., Marbate, P.: An efficient dynamic resource allocation strategy for VM environment in cloud. In: IEEE International Conference on Pervasive Computing (ICPC) (2015)
Domanal, S.G., Reddy, G.R.M.: Optimal load balancing in cloud computing by efficient utilization of virtual machines. In: 6th IEEE International Conference on Communication Systems and Networks (COMSNETS) (2014)
Fahim, Y., Lahmar, E.B., Labrlji, E.H., Eddaoui, A.: The load balancing based on the estimated finish time of tasks in cloud computing. In: 2nd IEEE International Conference
Haidri, R.A., Katti, C.P., Saxena, P.C.: A load balancing strategy for cloud computing environment. In: IEEE International Conference on Signal Propagation and Computer Technology (ICSPCT) (2014)
Bo, Z., Ji, G., Jieqing, A.: Cloud load balancing algorithm. In: 2nd IEEE International Conference on Information Science and Engineering (ICISE), pp. 5001–5004 (2010)
Dorigo, M., Blum, C.: Ant colony optimization theory: a survey. Theoret. Comput. Sci. 344(2), 243–278 (2005)
Sun, J., Xiong, S.-W., Guo, F.-M.: A new pheromone updating strategy in ant colony optimization. In: Machine Learning and Cybernetics, 2004. Proceedings of 2004 International Conference, vol. 1, pp. 620–625 (2004). IEEE
Mathiyalagan, P., Suriya, S., Sivanandam, S.N.: Modified ant colony algorithm for grid scheduling. Int. J. Comput. Sci. Eng. 2(02), 132–139 (2010)
Liu, A., Wang, Z.: Grid task scheduling based on adaptive ant colony algorithm. In: Management of e-Commerce and e-Government, 2008. ICMECG’08. International Conference, pp. 415–418 (2008). IEEE
MadadyarAdeh, M., Bagherzadeh, J.: An improved ant algorithm for grid scheduling problem using biased initial ants. In: Computer Research and Development (ICCRD), 2011 3rd International Conference, vol. 2, pp. 373–378 (2011). IEEE
Chen, W.-N., Zhang, J., Yu, Y.: Workflow scheduling in grids: an ant colony optimization approach. In: Evolutionary Computation, 2007. CEC 2007. IEEE Congress, pp. 3308–3315 (2007). IEEE
Pacinia, E., Mateosb, C., Garinoa, C.G.: Balancing throughput and response time in online scientific clouds via ant colony optimization. Adv. Eng. Softw. In press. Elsevier (2014)
Li, K., Xu, G., Zhao, G., Dong, Y., Wang, D.: Cloud task scheduling based on load balancing ant colony optimization. In: 2011 Sixth Annual ChinaGrid Conference, pp. 3–9 (2011). IEEE
Nusrat Pasha, D., Agarwal, A., Rastogi, R.: Round robin approach for VM load balancing algorithm in cloud computing environment. Int. J. Adv. Res. Comput. Sci. Softw. Eng. 4(5) (2014)
Razali, R.A.M., Ab Rahman, R., Zaini, N., Samad, M.: Virtual machine migration implementation in load balancing for cloud computing. In: 5th IEEE International Conference on Intelligent and Advanced Systems (ICIAS) (2014)
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We are thankful to Department of Computer Science and Engineering at Maharishi Markandeshwar Deemed-to-be University, Mullana, Ambala, for giving highly motivational supports.
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Kushwah, V.S., Goyal, S.K., Sharma, A. (2020). Maximize Resource Utilization Using ACO in Cloud Computing Environment for Load Balancing. In: Pant, M., Sharma, T., Verma, O., Singla, R., Sikander, A. (eds) Soft Computing: Theories and Applications. Advances in Intelligent Systems and Computing, vol 1053. Springer, Singapore. https://doi.org/10.1007/978-981-15-0751-9_54
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DOI: https://doi.org/10.1007/978-981-15-0751-9_54
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