Abstract
For the past several years, using cloud computing technology has become popular. With the cloud computing service providers, reducing the physical machine number providing resources for virtual service in cloud computing is one of the efficient ways to decrease the energy consumption amount which in turn enhance the performance of data centers. In this study, we propose the resource allocation problem to reduce the energy consumption. \(ECRA-SA\) algorithm was designed to solve and evaluate through CloudSim simulation tool compared with an FFD algorithm. The experimental results indicate that the proposed \(ECRA-SA\) algorithm yields a higher performance in comparison with an FFD algorithm.
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References
Arianyan, E., Taheri, H., Sharifian, S.: Novel energy and SLA efficient resource management heuristics for consolidation of virtual machines in cloud data centers. Computers Electrical Engineering 47, pp. 222–240. Elsever (2015).
Beloglazov, A., Buyya, R.: Optimal online deterministic algorithms and adaptive heuristics for energy and performance efficient dynamic consolidation of virtual machines in cloud data centers. Concurr. Comput. : Pract. EXper. 24(13), pp. 1397–1420. John Wiley and Sons Ltd (2012).
Calheiros, R.N. and et al.: Cloudsim: A toolkit for modeling and simulation of cloud computing environments and evaluation of resource provisioning algorithms. Softw. Pract. EXper. 41(1), pp. 23–50. John Wiley and Sons Ltd (2011).
Cao, Z., Dong, S.: Dynamic VM consolidation for energy-aware and SLA violation reduction in cloud computing. In: Parallel and Distributed Computing, Applications and Technologies (PDCAT), 2012 13th International Conference on, pp. 363–369. IEEE(2012).
Farahnakian, F. and et al.: Energy-aware dynamic VM consolidation in cloud data centers using ant colony system. In: Cloud Computing (CLOUD), 2014 IEEE 7th International Conference on, pp. 104–111. IEEE (2014).
Feller, E., Rilling, L., Morin, C.: Energy-aware ant colony based workload placement in clouds. In: Grid Computing (GRID), 2011 12th IEEE/ACM International Conference on, pp. 26–33. IEEE (2011).
Ha Huy Cuong Nguyen, et al.,: A New Technical Solution for Resource Allocation in Heterogeneous Distributed Platforms, in Advances in Digital Technologies, 2015, IOS Press, The Netherlands: The University of Macau, Macau. pp. 184–194 (2015).
Ha Huy Cuong Nguyen, et al.,: Deadlock Detection for Resource Allocation in Heterogeneous Distributed Platforms, in Recent Advances in Information and Communication Technology 2015, pp. 285–295. Springer (2015).
Luo, L. and et al.: A resource scheduling algorithm of cloud computing based on energy efficient optimization methods. In: Green Computing Conference (IGCC), 2012 International, pp. 16. IEEE (2012).
Quan, D.M. and et al.: Energy Efficient Resource Allocation Strategy for Cloud Data Centres, Computer and Information Sciences II: 26th International Symposium on Computer and Information Sciences, chap., pp. 133–141. Springer (2012).
Setzer, T., Stage, A.: Decision support for virtual machine reassignments in enterprise data centers. In: Network Operations and Management Symposium Workshops (NOMS Wksps), 2010 IEEE/IFIP, pp. 88–94. IEEE (2010).
Nguyen Minh Nhut Pham, Thu Huong Nguyen, Van Son Le: Resource Allocation for Virtual Service Based on Heterogeneous Shared Hosting Platforms. In: 8th Asian Conference ACIIDS 2016, Da Nang, Vietnam 2016, pp. 51–60. Springer (2016).
Kirkpatrick, S.: Optimization by simulated annealing: Quantitative studies. Journal of Statistical Physics 34(5), 975–986. Kluwer Academic Publishers-Plenum (1984).
Armbrust, M., et al.,: Above the Clouds: A Berkeley View of Cloud Computing.Technical Report No. UCB EECS-2009-28, the University of California at Berkley, USA, (2009).
Sotomayor, B.: Provisioning Computational resources using virtual machines and leases. Ph.D. Thesis submitted to The University of Chicago, USA, (2010).
Koomey, J.: Growth in data center electricity use 2005 to 2010. A report by Analytical Press, completed at the request of The New York Times, Vol. 9, (2011).
Ranjana, R., Raja, J.: A survey on power aware virtual machine placement strategies in a cloud data center. In 2013 International Conference on GreenComputing, Communication and Conservation of Energy (ICGCE), pp. 747–752, (2013).
Mitra, D., Romeo, F., and Vincentelli, A.S.: Convergence and Finite-Time Behavior of Simulated Annealing. Advances in Applied Probability, Vol. 18(3), pp. 747–771, (1986).
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Pham, N.M.N., Le, V.S., Nguyen, H.H.C. (2018). Energy-Efficient Resource Allocation for Virtual Service in Cloud Computing Environment. In: Bhateja, V., Nguyen, B., Nguyen, N., Satapathy, S., Le, DN. (eds) Information Systems Design and Intelligent Applications. Advances in Intelligent Systems and Computing, vol 672. Springer, Singapore. https://doi.org/10.1007/978-981-10-7512-4_13
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DOI: https://doi.org/10.1007/978-981-10-7512-4_13
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