A. Hooper, Green computing, Communication of the ACM, vol.51, issue.10, pp.11-13, 2008.

Y. Shao and D. Brooks, Energy characterization and instruction-level energy model of Intel's Xeon Phi processor, Proc. IEEE ISLPED, pp.389-394, 2013.

D. Kliazovich, P. Bouvry, and S. U. Khan, GreenCloud: a packet-level simulator of energy-aware cloud computing data centers, The Journal of Supercomputing, vol.62, issue.3, pp.1263-1283, 2012.

D. Kliazovich, P. Bouvry, and S. U. Khan, DENS: data center energy-efficient network-aware scheduling, Cluster computing, vol.16, issue.1, pp.65-75, 2013.

Y. C. Lee and A. Y. Zomaya, Energy efficient utilization of resources in cloud computing systems, The Journal of Supercomputing, vol.60, issue.2, pp.268-280, 2012.

J. Smith, A. Khajeh-hosseini, J. Ward, and I. Sommerville, Cloudmonitor:Profiling power usage, Proc. IEEE 5th CLOUD Comput, pp.947-948, 2012.

K. Bhavani, A. Hrishikesh, G. Ada, and S. Karsten, VM power metering: feasibility and challenges, ACM SIGMETRICSPerformance Evaluation Review, vol.38, pp.56-60, 2011.

T. Li and L. K. John, Run-time modeling and estimation of operatingsystem power consumption, Proc. ACM SIGMETRICS Int

, Conf.Meas. Model. Comput. Syst, pp.160-171, 2003.

N. T. Hieu, M. Di-francesco, and A. Ylä-jääski, Virtual machine consolidation with usage prediction for energy-efficient cloud data centers, IEEE 8th International Conference on, pp.750-757, 2015.

F. Farahnakian, P. Liljeberg, and J. Plosila, LiRCUP: Linear regression based CPU usage prediction algorithm for live migration of virtual machines in data centers, Software Engineering and Advanced Applications (SEAA), pp.357-364, 2013.

G. Dhiman, K. Mihic, and T. Rosing, A system for online power predictionin virtualized environments using Gaussian mixture models, inProc. 47th DAC, pp.807-812, 2010.