Abstract
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.
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References
Wenwu, Z., Chong, L., Jianfeng, W., Shipeng, L.: Multimedia Cloud Computing. IEEE Signal Processing Magazine 28, 59–69 (2011)
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)
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)
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)
Hines, M.R., Deshpande, U., Gopalan, K.: Post-copy live migration of virtual machines. SIGOPS Oper. Syst. Rev. 43, 14–26 (2009)
Zorov, D.B., Kobrinsky, E., Juhaszova, M., Sollott, S.J.: Examining Intracellular Organelle Function Using Fluorescent Probes: From Animalcules to Quantum Dots. Circulation Research 95, 239–252 (2004)
Banerjee, P., Friedrich, R., Bash, C., Goldsack, P., Huberman, B.A., Manley, J., et al.: Everything as a Service: Powering the New Information Economy. Computer 44, 36–43 (2011)
Musa, I.K., Stuart, W.: A Converged Service Plane for Virtual Infrastructure Containers. IJCSI International Journal of Computer Science 10, 12 (2013)
Thomas, F.J.M.: The Biogenesis of Cellular Organelles. Plenum Publishers (2005)
James Frey, T.T., Foster, I., Livny, M., Tuecke, S.: Condor-G: A Computation Management Agent for Multi-Institutional Grids. Journal of Cluster Computing 5, 237–246 (2002)
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)
Rothlauf, F.: Design of modern heuristics principles and application. In: Natural Computing. Springer, Berlin (2011)
Kramer, O.: Self-adaptive heuristics for evolutionary computation. SCI, vol. 147. Springer, Heidelberg (2008)
Donoso, Y., Fabregat, R.: Multi-objective optimization in computer networks using metaheuristics. Auerbach Publications, Boca Raton (2007)
Liu, D.S., Tan, K.C., Huang, S.Y., Goh, C.K., Ho, W.K.: On solving multiobjective bin packing problems using evolutionary particle swarm optimization. European Journal of Operational Research 190, 357–382 (2008)
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)
Deb, K., Pratap, A., Agarwal, S., Meyarivan, T.: A fast and elitist multiobjective genetic algorithm: NSGA-II. IEEE Transactions on Evolutionary Computation 6, 182–197 (2002)
Durillo, J.J., Nebro, A.J.: jMetal: a Java Framework for Multi-Objective Optimization. In: Advances in Engineering Software, pp. 760–771 (2011)
Reeves, C.R.: Modern heuristic techniques for combinatorial problems. Blackwell Scientific Publications, London (1993)
Moulton, C.M.: Hierarchical Clustering of Evolutionary Multiobjective Programming Results to Inform Land Use Planning (2007)
Garey, M.D., Johnson, D.S.: Computers and Intractability: A Guide to the Theory of NP-Completeness. Freeman, CA (1979)
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)
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Musa, I.K., Stuart, W. (2014). Multi Objective Optimization Strategy Suitable for Virtual Cells as a Service. In: Abraham, A., Krömer, P., Snášel, V. (eds) Innovations in Bio-inspired Computing and Applications. Advances in Intelligent Systems and Computing, vol 237. Springer, Cham. https://doi.org/10.1007/978-3-319-01781-5_5
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DOI: https://doi.org/10.1007/978-3-319-01781-5_5
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-01780-8
Online ISBN: 978-3-319-01781-5
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