Load Balancing of Unbalanced Matrix Problem with More Machines
In nowadays cloud computing as a developing web accommodation model has been propagating to offer different Internet resources to users. Cloud computing occupies a range of computing Internet applications for facilitating the finishing of sizable voluminous-scale tasks. Cloud computing is a web predicated distributed computing. There is more than a million number of servers connected to the Internet to provide several types of accommodations to provide cloud users. Constrained numbers of servers execute fewer numbers tasks at a time. So it is not too easy to execute all tasks at a time. Some systems execute all tasks, so there are needed to balance all loads. Load balance minimizes the completion time as well as executes all tasks a particular way.
There are not possible to remain equal number servers to execute equal tasks. Tasks to be executed in cloud computing would be less than the connected servers sometime. Excess servers have to execute a fewer number of tasks. Here we are going to present an algorithm for load balancing and performance with minimization completion time and throughput. We apply here a very famous Hungarian method to balance all loads in distributing computing. Hungarian Technique helps us to minimize the cost matrix problem.
KeywordsLoad balancing Load balancing algorithms Cloud computing Hungarian method
- 1.Mell, P., Grance, T.: The NIST definition of cloud computing, pp. 20–23 (2011)Google Scholar
- 3.Ritchie, G., Levine, J.: A fast, effective local search for scheduling independent works in heterogeneous computing environments. J. Comput. Appl. 25, 1190–1192 (2005)Google Scholar
- 5.Wang, S.C., Yan, K.Q., Liao, W.P., Wang, S.S.: Towards a load balancing in a three-level cloud computing network. In: CSIT, pp. 108—113 (2010)Google Scholar
- 6.Hung, C.-L., Wang, H.-H., Hu, Y.-C.: Efficient load balancing algorithm for cloud computing network. In: International Conference on Information Science and Technology (IST 2012), pp. 28–30, April 2012Google Scholar
- 7.Kokilavani, T., Amalarethinam, D.G.: Load balanced min-min algorithm for static meta-task scheduling in grid computing. Int. J. Comput. Appl. 20(2), 43–49 (2011)Google Scholar
- 8.Wu, M.-Y., Shu, W., Zhang, H.: Segmented min-min: a static mapping algorithm for meta-tasks on heterogeneous computing systems. In: Proceedings of the 9th Heterogeneous Computing Workshop (HCW 2000), pp. 375–385. IEEE (2000)Google Scholar
- 9.Mondal, R.K., et al.: Load balancing with job switching in cloud computing network. In: Satapathy, S.C., Bhateja, V., Udgata, S.K., Pattnaik, P.K. (eds.) Proceedings of the 5th International Conference on Frontiers in Intelligent Computing: Theory and Applications. AISC, vol. 516, pp. 305–312. Springer, Singapore (2017). https://doi.org/10.1007/978-981-10-3156-4_31CrossRefGoogle Scholar
- 10.Mondal, R.K., Ray, P., Nandi, E., Biswas, B., Sanyal, M.K., Sarddar, D.: Load balancing of unbalanced matrix with hungarian method. In: Mandal, J.K., Dutta, P., Mukhopadhyay, S. (eds.) CICBA 2017. CCIS, vol. 776, pp. 256–270. Springer, Singapore (2017). https://doi.org/10.1007/978-981-10-6430-2_20CrossRefGoogle Scholar
- 11.Mondal, R.K., Ray, P., Nandi, E., Sen, P., Sarddar, D.: Load balancing of the unbalanced cost matrix in a cloud computing network. In: Computer, Communication and Electrical Technology: Proceedings of the International Conference on Advancement of Computer Communication and Electrical Technology (ACCET 2016), West Bengal, India, 21–22 October 2016, p. 81. CRC Press (2017)Google Scholar