Multi Objective Optimization Strategy Suitable for Virtual Cells as a Service
Performance guarantee and management complexity are critical issues in delivering next generation infrastructure as a service (IAAS) cloud computing model. This is normally attributed to the current size of datacenters that are built to enable the cloud services. A promising approach to handle these issues is to offer IAAS from a subset of the datacenter as a, biologically inspired, virtual service cell. However, this approach requires effective strategies to ensure efficient use of datacenter resources while maintaining high performance and functionality for the service cells. We present a multi-objective and multi-constraint optimization (MOMCO) strategy based on genetic algorithm to the problem of resource placement and utilization suitable for virtual service cell model. We apply a combination of NSGA-II with various crossover strategies and population sizes to test our optimization strategy. Results obtained from our simulation experiment shows significant improvement on acceptance rate over non optimized solutions.
KeywordsCloud computing IAAS biologically-inspired NGSA-II
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- 2.Buyya, R., Yeo, C.S., Venugopal, S., Broberg, J., Brandic, I.: Cloud computing and emerging IT platforms: Vision, hype, and reality for delivering computing as the 5th utility. Future Generation Computer Systems-the International Journal of Grid Computing-Theory Methods and Applications 25, 599–616 (2009)CrossRefGoogle Scholar
- 3.Michael, A., Armando, F., Rean, G., Joseph, A.D., Katz, R.H., Andrew, K., et al.: Above the Clouds: A Berkeley View of Cloud Computing. Commun. ACM (2009)Google Scholar
- 4.Theophilus, B., Aditya, A., Maltz, D.A.: Network traffic characteristics of data centers in the wild. Presented at the Proceedings of the 10th ACM SIGCOMM Conference on Internet Measurement, Melbourne, Australia (2010)Google Scholar
- 8.Musa, I.K., Stuart, W.: A Converged Service Plane for Virtual Infrastructure Containers. IJCSI International Journal of Computer Science 10, 12 (2013)Google Scholar
- 9.Thomas, F.J.M.: The Biogenesis of Cellular Organelles. Plenum Publishers (2005)Google Scholar
- 11.Junlin, C., Wei, Z., Jing, Z., Wei, W.: Design of cloud model controller based on multi-objective optimization. In: Control and Decision Conference (CCDC), pp. 19–24 (2011)Google Scholar
- 12.Rothlauf, F.: Design of modern heuristics principles and application. In: Natural Computing. Springer, Berlin (2011)Google Scholar
- 16.Fernández, A., Gil, C., Márquez, A.L., Baños, R., Montoya, M.G., Parra, M.: A memetic algorithm for two-dimensional multi-objective bin-packing with constraints. In: Proceedings of the 13th Annual Conference Companion on Genetic and Evolutionary Computation, pp. 341–346 (2011)Google Scholar
- 18.Durillo, J.J., Nebro, A.J.: jMetal: a Java Framework for Multi-Objective Optimization. In: Advances in Engineering Software, pp. 760–771 (2011)Google Scholar
- 20.Moulton, C.M.: Hierarchical Clustering of Evolutionary Multiobjective Programming Results to Inform Land Use Planning (2007)Google Scholar
- 22.Naveen, K., Karambir, R.K.: A Comparative Analysis of PMX, CX and OX Crossover operators for solving Travelling Salesman Problem. International Journal of Latest Research in Science and Technology 1, 98–101 (2012)Google Scholar