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[2] A. Beloglazov, J. Abawajy, and R. Buyya, “Energy-aware resource allocation heuristics for efficient management of data centers for cloud computing,†Future Generation Computer Systems, vol. 28, no. 5, pp. 755–768, 2012.
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[4] I. Takouna, W. Dawoud, and C. Meinel, “Energy efficient scheduling of hpc-jobs on virtualize clusters using host and vm dynamic configuration,†ACM SIGOPS Operating Systems Review, vol. 46, no. 2, pp. 19–27, 2012.
[5] L. A. Barroso and U. Holzle, “The case for energy-proportional computing,†¨ IEEE computer, vol. 40, no. 12, pp. 33–37, 2007.
[6] M. D. De Assuncao, J.-P. Gelas, L. Lefevre, and A.-C. Orgerie, “The green grid5000: Instrumenting and using a grid with energy sensors,†in Remote Instrumentation for eScience and Related Aspects. Springer, 2012, pp. 25–42.
[7] C. Hyser, B. Mckee, R. Gardner, and B. J. Watson, “Autonomic virtual machine placement in the data center,†Hewlett Packard Laboratories, Tech. Rep. HPL-2007-189, pp. 2007–189, 2007.
[8] J. T. Piao and J. Yan, “A network-aware virtual machine placement and migration approach in cloud computing,†in Grid and Cooperative Computing (GCC), 2010 9th International Conference on. IEEE, 2010, pp. 87–92.
[9] X. Meng, V. Pappas, and L. Zhang, “Improving the scalability of data center networks with traffic-aware virtualmachine placement,†in INFOCOM, 2010 Proceedings IEEE. IEEE, 2010, pp. 1–9.
[10] J. W. Jiang, T. Lan, S. Ha, M. Chen, and M. Chiang, “Joint vm placement and routing for data center traffic engineering,†in INFOCOM, 2012 Proceedings IEEE. IEEE, 2012, pp. 2876–2880.
[11] K. Park and V. S. Pai, “Comon: a mostly-scalable monitoring system for planetlab,†ACM SIGOPS Operating Systems Review, vol. 40, no. 1, pp. 65–74, 2006.
[12] X. Fan, W.-D. Weber, and L. A. Barroso, “Power provisioning for a warehouse-sized computer,†in ACM SIGARCH Computer Architecture News, vol. 35, no. 2. ACM, 2007, pp. 13–23.
[13] R. N. Calheiros, R. Ranjan, A. Beloglazov, C. A. De Rose, and R. Buyya, “Cloudsim: a toolkit for modeling and simulation of cloud computing environments and evaluation of resource provisioning algorithms,†Software: Practice and Experience, vol. 41, no. 1, pp. 23–50, 2011.
[14] Amazon, “Amazon ec2 instance types,†http://aws.amazon.com/ec2/.
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Affiliations
Amin Rahimi
Department of Electrical and Computer Engineering, University of Tabriz, Tabriz, Iran
Leili Mohammad Khanli
Department of Electrical and Computer Engineering, University of Tabriz, Tabriz, Iran
Saeid Pashazadeh
Department of Electrical and Computer Engineering, University of Tabriz, Tabriz, Iran
Energy efficient virtual machine placement algorithm with balanced resource utilization based on priority of resources
Abstract
The increasing energy consumption has become a major concern in cloud computing due to its cost and environmental damage. Virtual Machine placement algorithms have been proven to be very effective in increasing energy efficiency and thus reducing the costs. In this paper we have introduced a new priority routing VM placement algorithm and have compared it with PABFD (power-aware best fit decreasing) on CoMon dataset using CloudSim for simulation. Our experiments show the superiority of our new method with regards to energy consumption and level of SLA violations measures and prove that priority routing VM placement algorithm can be effectively utilized to increase energy efficiency in the clouds.