A. Patrizio, 10 predictions for the data center and the cloud in 2019, Network World Journal, 2018.

J. Wilkes, More Google cluster data, Google research blog, 2011.

M. Dayarathna, Y. Wen, and R. Fan, Data center energy consumption modeling: a survey, IEEE Communications Surveys & Tutorials, vol.18, issue.1, 2016.

A. Beloglazov, R. Buyya, Y. C. Lee, and A. Y. Zomaya, A taxonomy and survey of energy-efficient data centers and cloud computing systems, 2010.

F. Chen, J. Grundy, Y. Yang, J. Schneider, and Q. He, Experimental analysis of task-based energy consumption in cloud computing systems, Proc. 4th ACM/SPEC ICPE, pp.295-306, 2013.

P. Xiao, Z. Hu, D. Liu, G. Yan, and X. Qu, Virtual machine power measuring technique with bounded error in cloud environments, J. Netw. Comput. Appl, vol.36, issue.2, pp.818-828, 2013.

C. Reiss, J. Wilkes, and J. L. Hellerstein, Google cluster-usage traces: format + schema, 2011.

C. Reiss, A. Tumanov, G. R. Ganger, R. H. Katz, and M. A. Kozuch, Heterogeneity and dynamicity of clouds at scale: Google trace analysis, ACM Symposium on Cloud Computing (SoCC), 2012.

S. Di, D. Kondo, and W. Cirne, Host Load Prediction in a Google Compute Cloud with a Bayesian Model, Proceedings of the International Conference on High Performance Computing, Networking, Storage and Analysis, SC'12, 2012.
URL : https://hal.archives-ouvertes.fr/hal-00788002

O. Beaumont, L. Eyraud-dubois, and J. A. Lorenzo-del-castillo, Analyzing real cluster data for formulating allocation algorithms in Cloud platforms, 2nd International Symposium on Computer architecture and High Performance Computing (SBAC-PAD), 2014.
URL : https://hal.archives-ouvertes.fr/hal-01214636

M. Alam, K. A. Shakil, and S. Sethi, Analysis and Clustering of Workload in Google Cluster Trace based on Resource Usage, 2015.

P. Minet, E. Renault, I. Khoufi, and S. Boumerdassi, Analyzing traces from a Google data center, 14th International Wireless Communications and Mobile Computing Conference (IWCMC 2018), 2018.
URL : https://hal.archives-ouvertes.fr/hal-01870216

B. Liu, Y. Lin, and Y. Chen, Quantitative workload analysis and prediction using Google cluster traces, 2016 IEEE Conference on Computer Communications Workshops (INFOCOM WKSHPS), pp.935-940, 2016.

J. Prevost, K. Nagothu, B. Kelley, and M. Jamshidi, Prediction of cloud data center networks loads using stochastic and neural models, 6th International Conference on System of Systems Engineering, pp.276-281, 2011.

M. S. Yoon, A. E. Kamal, and Z. Zhu, Adaptive data center activation with user request prediction, Computer Networks, vol.122, pp.191-204, 2017.

S. Mazumdar and A. S. Kumar, Forecasting Data Center Resource Usage: An Experimental Comparison with Time-Series Methods, Proceedings of the Eighth International Conference on Soft Computing and Pattern Recognition, 2016.

R. Cao, Z. Yu, T. Marbach, J. Li, G. Wang et al., Load Prediction for Data Centers Based on Database Service, IEEE 42nd Annual Computer Software and Applications Conference (COMPSAC), 2018.

T. Hastie, R. Tibshirani, and J. Friedman, The Elements of Statistical Learning: Data Mining, Inference, and Prediction, 2009.

H. Kantz and T. Schreiber, Nonlinear Time Series Analysis, 2004.

S. Caires and J. A. Ferreira, On the Non-parametric Prediction of Conditionally Stationary Sequences. Statistical Inference for Stochastic Processes, vol.8, pp.151-184, 2005.

A. Papoulis, Probability, Random Variables and Stochastic Process, 1965.

A. Wolke, M. Bichler, and T. Setzer, Planning vs. dynamic control: Resource allocation in corporate clouds, IEEE Transactions on Cloud Computing, vol.7161, issue.99, pp.1-14, 2014.

C. Delimitrou and C. Kozyrakis, Quasar: Resource-efficient and QoSaware Cluster Management, Proceedings of the 19th International Conference on Architectural Support for Programming Languages and Operating Systems, ASPLOS'14, pp.127-144, 2014.

Q. Zhang, M. Zhani, S. Zhang, Q. Zhu, R. Boutaba et al., Dynamic energy-aware capacity provisioning for cloud computing environments, Proceedings of the 9th international conference on Autonomic computing (ICAC '12), pp.145-154, 2012.

Q. Zhang, M. F. Zhani, R. Boutaba, and J. L. Hellerstein, Dynamic Heterogeneity-Aware Resource Provisioning in the Cloud, IEEE Transactions on Cloud Computing, vol.2, issue.1, pp.14-28, 2014.

M. Dabbagh, B. Hamdaoui, M. Guizani, and A. Rayes, Energy-Efficient Resource Allocation and Provisioning Framework for Cloud Data Centers, IEEE Transactions on Network and Service Management, vol.12, issue.3, pp.377-391, 2015.

P. J. Brockwell and R. A. Davis, Time Series: Theory and Methods, 1986.

C. S. Burrus, J. A. Barreto, and I. W. Selesnick, Iterative reweighted least squares design of filters, IEEE Transactions on Signal Processing, vol.42, issue.11, pp.2926-2936, 1994.

T. Söderström and P. Stoica, System Identification. Prentica Hall International, 1989.

I. Sarji, C. Ghali, A. Chehab, and A. Kayssi, CloudESE: Energy Efficiency Model for Cloud Computing Environments, International Conference on Energy Aware Computing (ICEAC), 2011.