M. Amiri and L. Mohammad-khanli, Survey on prediction models of applications for resources provisioning in cloud, Journal of Network and Computer Applications, vol.82, pp.93-113, 2017.

Y. Bengio, P. Simard, and P. Frasconi, Learning long-term dependencies with gradient descent is difficult, IEEE transactions on neural networks, vol.5, issue.2, pp.157-166, 1994.
DOI : 10.1109/72.279181
URL : http://www.research.microsoft.com/~patrice/PDF/long_term.pdf

F. Benhammadi, Z. Gessoum, and A. Mokhtari, Cpu load prediction using neuro-fuzzy and bayesian inferences, Neurocomputing, vol.74, issue.10, pp.1606-1616, 2011.
URL : https://hal.archives-ouvertes.fr/hal-01169910

F. Benson, A note on the estimation of mean and standard deviation from quantiles, Journal of the Royal Statistical Society. Series B (Methodological), vol.11, issue.1, pp.91-100, 1949.

G. Bontempi, Long term time series prediction with multi-input multioutput local learning, Proc. 2nd ESTSP, pp.145-154, 2008.

L. Breiman, Random forests. Machine learning, vol.45, pp.5-32, 2001.

R. Caruana and A. Niculescu-mizil, An empirical comparison of supervised learning algorithms, Proceedings of the 23rd international conference on Machine learning, pp.161-168, 2006.
DOI : 10.1145/1143844.1143865
URL : http://www.eecs.wsu.edu/~holder/courses/CptS570/fall07/present/CaruanaICML06.pdf

M. Carvalho, W. Cirne, F. Brasileiro, and J. Wilkes, Long-term slos for reclaimed cloud computing resources, Proceedings of the ACM Symposium on Cloud Computing, pp.1-13, 2014.
DOI : 10.1145/2670979.2670999
URL : http://dl.acm.org/ft_gateway.cfm?id=2670999&type=pdf

C. Delimitrou and C. Kozyrakis, Hcloud: Resource-efficient provisioning in shared cloud systems, ACM SIGOPS Operating Systems Review, vol.50, issue.2, pp.473-488, 2016.

S. Di, D. Kondo, and W. Cirne, Host load prediction in a google compute cloud with a bayesian model, High Performance Computing, Networking, Storage and Analysis (SC), 2012 International Conference for, pp.1-11, 2012.
URL : https://hal.archives-ouvertes.fr/hal-00788002

P. A. Dinda and D. R. O'hallaron, An evaluation of linear models for host load prediction, High Performance Distributed Computing, pp.87-96, 1999.

T. V. Duy, Y. Sato, and Y. Inoguchi, Improving accuracy of host load predictions on computational grids by artificial neural networks, International Journal of Parallel, Emergent and Distributed Systems, vol.26, issue.4, pp.275-290, 2011.

J. Friedman, T. Hastie, and R. Tibshirani, The elements of statistical learning, Springer series in statistics, vol.1, 2001.

J. H. Friedman, Greedy function approximation: a gradient boosting machine, Annals of statistics, pp.1189-1232, 2001.

R. Garg, H. Saran, R. S. Randhawa, and M. Singh, A sla framework for qos provisioning and dynamic capacity allocation, Tenth IEEE International Workshop on, pp.129-137, 2002.
DOI : 10.1109/iwqos.2002.1006581
URL : http://www.cse.iitd.ernet.in/~rahul/mypapers/sla.iwqos.ps.gz

D. Gmach, J. Rolia, L. Cherkasova, and A. Kemper, Workload analysis and demand prediction of enterprise data center applications, IEEE 10th International Symposium on, pp.171-180, 2007.
DOI : 10.1109/iiswc.2007.4362193
URL : http://www-db.in.tum.de/research/publications/conferences/iiswc2007.pdf

S. Goh, Design-adaptive nonparametric estimation of conditional quantile derivatives, Journal of Nonparametric Statistics, 2012.
DOI : 10.1080/10485252.2012.688826
URL : http://faculty.chicagobooth.edu/midwest.econometrics/Papers/MEGGoh.pdf

, Preemptible virtual machines, 2018.

P. Han, X. Zhang, R. S. Norton, and Z. Feng, Large-scale prediction of long disordered regions in proteins using random forests, BMC bioinformatics, vol.10, issue.1, p.1, 2009.

S. Hochreiter and J. Schmidhuber, Long short-term memory, Neural computation, vol.9, issue.8, pp.1735-1780, 1997.

S. Islam, J. Keung, K. Lee, and A. Liu, Empirical prediction models for adaptive resource provisioning in the cloud, Future Generation Computer Systems, vol.28, issue.1, pp.155-162, 2012.

M. A. Knefati, A. Oulidi, and B. Abdous, Local linear double and asymmetric kernel estimation of conditional quantiles, Communications in Statistics-Theory and Methods, vol.45, issue.12, pp.3473-3488, 2016.

R. Koenker and G. Bassett, Regression quantiles, Econometrica: journal of the Econometric Society, pp.33-50, 1978.

J. Kumar, R. Goomer, and A. K. Singh, Long short term memory recurrent neural network (lstm-rnn) based workload forecasting model for cloud datacenters, Procedia Computer Science, vol.125, pp.676-682, 2018.

P. Marshall, K. Keahey, and T. Freeman, Improving utilization of infrastructure clouds, Cluster, Cloud and Grid Computing (CCGrid), 2011 11th IEEE/ACM International Symposium on, pp.205-214, 2011.

N. Meinshausen, Quantile regression forests, Journal of Machine Learning Research, vol.7, pp.983-999, 2006.

X. Meng, C. Isci, J. Kephart, L. Zhang, E. Bouillet et al., Efficient resource provisioning in compute clouds via vm multiplexing, Proceedings of the 7th international conference on Autonomic computing, pp.11-20, 2010.

M. Shaari, T. F. Ang, L. Y. Por, and C. S. Liew, Dynamic pricing scheme for resource allocation in multi-cloud environment, Malaysian Journal of Computer Science, vol.30, issue.1, 2017.

B. Song, Y. Yu, Y. Zhou, Z. Wang, and S. Du, Host load prediction with long short-term memory in cloud computing, The Journal of Supercomputing, pp.1-15, 2017.

A. Spark, Apache spark?-lightning-fast cluster computing, 2014.

S. B. Taieb and A. F. Atiya, A bias and variance analysis for multistepahead time series forecasting, IEEE transactions on neural networks and learning systems, vol.27, pp.62-76, 2016.

Y. Wu, Y. Yuan, G. Yang, and W. Zheng, Load prediction using hybrid model for computational grid, 8th IEEE/ACM International Conference on, pp.235-242, 2007.

Q. Yang, C. Peng, H. Zhao, Y. Yu, Y. Zhou et al., A new method based on psr and ea-gmdh for host load prediction in cloud computing system, The Journal of Supercomputing, vol.68, issue.3, pp.1402-1417, 2014.

Q. Yang, Y. Zhou, Y. Yu, J. Yuan, X. Xing et al., Multi-step-ahead host load prediction using autoencoder and echo state networks in cloud computing, The Journal of Supercomputing, vol.71, issue.8, pp.3037-3053, 2015.