E. Abrahám, F. Corzilius, E. B. Johnsen, G. Kremer, and J. Mauro, Zephyrus2: On the fly deployment optimization using SMT and CP technologies, Proceedings of the 2nd International Symposium on Dependable Software Engineering, pp.229-245, 2016.

E. Ahvar, S. Ahvar, Z. A. Mann, N. Crespi, J. Garcia-alfaro et al., CACEV: A Cost and Carbon Emission-Efficient Virtual Machine Placement Method for Green Distributed Clouds, 2016 IEEE International Conference on Services Computing (SCC), pp.275-282, 2016.
DOI : 10.1109/SCC.2016.43

URL : https://hal.archives-ouvertes.fr/hal-01359868

D. Bartók and Z. A. Mann, A branch-and-bound approach to virtual machine placement, Proceedings of the 3rd HPI Cloud Symposium " Operating the Cloud, pp.49-63, 2015.

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, issue.5, pp.755-768, 2012.
DOI : 10.1016/j.future.2011.04.017

A. Beloglazov and R. Buyya, Optimal online deterministic algorithms and adaptive heuristics for energy and performance efficient dynamic consolidation of virtual machines in Cloud data centers, Concurrency and Computation: Practice and Experience, vol.44, issue.3, pp.1397-1420, 2012.
DOI : 10.1145/1508284.1508269

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.43, issue.4, pp.23-50, 2011.
DOI : 10.1109/MC.2010.111

R. D. Cosmo, M. Lienhardt, R. Treinen, S. Zacchiroli, J. Zwolakowski et al., Automated synthesis and deployment of cloud applications, Proceedings of the 29th ACM/IEEE international conference on Automated software engineering, ASE '14, pp.211-222, 2014.
DOI : 10.1145/2642937.2642980

. Digital-power and . Group, The cloud begins with coal ? Big data, big networks, big infrastructure, and big power, 2013.

Y. Gao, H. Guan, Z. Qi, Y. Hou, and L. Liu, A multi-objective ant colony system algorithm for virtual machine placement in cloud computing, Journal of Computer and System Sciences, vol.79, issue.8, pp.1230-1242, 2013.
DOI : 10.1016/j.jcss.2013.02.004

M. Garca-valls, T. Cucinotta, and C. Lu, Challenges in real-time virtualization and predictable cloud computing, Journal of Systems Architecture, vol.60, issue.9, pp.726-740, 2014.
DOI : 10.1016/j.sysarc.2014.07.004

D. Gmach, J. Rolia, L. Cherkasova, G. Belrose, T. Turicchi et al., An integrated approach to resource pool management: Policies, efficiency and quality metrics, 2008 IEEE International Conference on Dependable Systems and Networks With FTCS and DCC (DSN), pp.326-335, 2008.
DOI : 10.1109/DSN.2008.4630101

URL : http://www.hpl.hp.com/techreports/2008/HPL-2008-89.pdf

C. P. Gomes and B. Selman, Algorithm portfolios, Artificial Intelligence, vol.126, issue.1-2, pp.43-62, 2001.
DOI : 10.1016/S0004-3702(00)00081-3

M. Guazzone, C. Anglano, and M. Canonico, Exploiting VM Migration for the Automated Power and Performance Management of Green Cloud Computing Systems, 1st International Workshop on Energy Efficient Data Centers, pp.81-92, 2012.
DOI : 10.1007/978-3-642-33645-4_8

B. Guenter, N. Jain, and C. Williams, Managing cost, performance, and reliability tradeoffs for energy-aware server provisioning, 2011 Proceedings IEEE INFOCOM, pp.1332-1340, 2011.
DOI : 10.1109/INFCOM.2011.5934917

URL : http://research.microsoft.com/en-us/um/people/bguenter/docs/guenter11managing.pdf

B. A. Huberman, R. M. Lukose, and T. Hogg, An Economics Approach to Hard Computational Problems, Science, vol.275, issue.5296, pp.51-54, 1997.
DOI : 10.1126/science.275.5296.51

C. Hyser, B. Mckee, R. Gardner, and B. J. Watson, Autonomic virtual machine placement in the data center, 2008.

G. Jung, M. A. Hiltunen, K. R. Joshi, R. D. Schlichting, and C. Pu, Mistral: Dynamically Managing Power, Performance, and Adaptation Cost in Cloud Infrastructures, 2010 IEEE 30th International Conference on Distributed Computing Systems, pp.62-73, 2010.
DOI : 10.1109/ICDCS.2010.88

H. Li, G. Zhu, C. Cui, H. Tang, Y. Dou et al., Energy-efficient migration and consolidation algorithm of virtual machines in data centers for cloud computing, Computing, vol.41, issue.1, pp.303-317, 2016.
DOI : 10.1002/spe.995

R. Li, Q. Zheng, X. Li, and J. Wu, A Novel Multi-objective Optimization Scheme for Rebalancing Virtual Machine Placement, 2016 IEEE 9th International Conference on Cloud Computing (CLOUD), pp.710-717, 2016.
DOI : 10.1109/CLOUD.2016.0099

W. Li, J. Tordsson, and E. Elmroth, Virtual Machine Placement for Predictable and Time-Constrained Peak Loads, Proceedings of the 8th International Conference on Economics of Grids, Clouds, Systems, and Services, pp.120-134, 2011.
DOI : 10.1007/978-3-642-28675-9_9

Z. Li, C. Yan, X. Yu, and N. Yu, Bayesian network-based Virtual Machines consolidation method, Future Generation Computer Systems, vol.69, pp.75-87, 2017.
DOI : 10.1016/j.future.2016.12.008

Z. A. Mann, Allocation of Virtual Machines in Cloud Data Centers???A Survey of Problem Models and Optimization Algorithms, ACM Computing Surveys, vol.48, issue.1, 2015.
DOI : 10.1007/s10586-008-0067-6

Z. A. Mann, Approximability of virtual machine allocation: much harder than bin packing, Proceedings of the 9th Hungarian-Japanese Symposium on Discrete Mathematics and Its Applications, pp.21-30, 2015.

Z. A. Mann, Modeling the virtual machine allocation problem, Proceedings of the International Conference on Mathematical Methods, Mathematical Models and Simulation in Science and Engineering, pp.102-106, 2015.

Z. A. Mann, Multicore-Aware Virtual Machine Placement in Cloud Data Centers, IEEE Transactions on Computers, vol.65, issue.11, pp.3357-3369, 2016.
DOI : 10.1109/TC.2016.2529629

Z. ´. Mann and M. Szabó, Which is the best algorithm for virtual machine placement optimization? Concurrency and Computation, Practice and Experience, vol.29, issue.10, 2017.
DOI : 10.1002/cpe.4083

A. Marotta and S. Avallone, A Simulated Annealing Based Approach for Power Efficient Virtual Machines Consolidation, 2015 IEEE 8th International Conference on Cloud Computing, pp.445-452, 2015.
DOI : 10.1109/CLOUD.2015.66

M. Mishra and A. Sahoo, On Theory of VM Placement: Anomalies in Existing Methodologies and Their Mitigation Using a Novel Vector Based Approach, 2011 IEEE 4th International Conference on Cloud Computing, pp.275-282, 2011.
DOI : 10.1109/CLOUD.2011.38

C. Qu, R. N. Calheiros, and R. Buyya, Mitigating impact of short-term overload on multi-cloud web applications through geographical load balancing, Concurrency and Computation: Practice and Experience, 2017.
DOI : 10.1145/2662112

S. Rampersaud and D. Grosu, Sharing-Aware Online Algorithms for Virtual Machine Packing in Cloud Environments, 2015 IEEE 8th International Conference on Cloud Computing, pp.718-725, 2015.
DOI : 10.1109/CLOUD.2015.100

B. C. Ribas, R. M. Suguimoto, R. A. Montano, F. Silva, L. De-bona et al., On Modelling Virtual Machine Consolidation to Pseudo-Boolean Constraints, 13th Ibero-American Conference on AI, pp.361-370, 2012.
DOI : 10.1007/978-3-642-34654-5_37

M. A. Salehi, P. R. Krishna, K. S. Deepak, and R. Buyya, Preemption-Aware Energy Management in Virtualized Data Centers, 2012 IEEE Fifth International Conference on Cloud Computing, pp.844-851, 2012.
DOI : 10.1109/CLOUD.2012.147

URL : http://www.gridbus.org/papers/CloudEnergyManagement-Cloud2012.pdf

L. Shi, J. Furlong, and R. Wang, Empirical evaluation of vector bin packing algorithms for energy efficient data centers, IEEE Symposium on Computers and Communications, pp.9-15, 2013.

A. Strunk, Costs of Virtual Machine Live Migration: A Survey, 2012 IEEE Eighth World Congress on Services, pp.323-329, 2012.
DOI : 10.1109/SERVICES.2012.23

P. Svärd, W. Li, E. Wadbro, J. Tordsson, and E. Elmroth, Continuous Datacenter Consolidation, 2015 IEEE 7th International Conference on Cloud Computing Technology and Science (CloudCom), pp.387-396, 2015.
DOI : 10.1109/CloudCom.2015.11

L. Tomás and J. Tordsson, An Autonomic Approach to Risk-Aware Data Center Overbooking, IEEE Transactions on Cloud Computing, vol.2, issue.3, pp.292-305, 2014.
DOI : 10.1109/TCC.2014.2326166

A. Verma, G. Dasgupta, T. K. Nayak, P. De, and R. Kothari, Server workload analysis for power minimization using consolidation, Proceedings of the 2009 USENIX Annual Technical Conference, pp.355-368, 2009.

T. Wood, P. Shenoy, A. Venkataramani, and M. Yousif, Sandpiper: Black-box and gray-box resource management for virtual machines, Computer Networks, vol.53, issue.17, pp.2923-2938, 2009.
DOI : 10.1016/j.comnet.2009.04.014

Z. Xiao, W. Song, and Q. Chen, Dynamic Resource Allocation Using Virtual Machines for Cloud Computing Environment, IEEE Transactions on Parallel and Distributed Systems, vol.24, issue.6, pp.1107-1117, 2013.
DOI : 10.1109/TPDS.2012.283

URL : http://citeseerx.ist.psu.edu/viewdoc/summary?doi=

L. Xu, F. Hutter, H. H. Hoos, and K. Leyton-brown, SATzilla: portfolio-based algorithm selection for SAT, Journal of Artificial Intelligence Research, vol.32, pp.565-606, 2008.

Z. Zhang, C. C. Hsu, and M. Chang, CoolCloud: A practical dynamic virtual machine placement framework for energy aware data centers, Proceedings of the 8th IEEE International Conference on Cloud Computing, pp.758-765, 2015.
DOI : 10.1109/cloud.2015.105

Q. Zheng, R. Li, X. Li, and J. Wu, A Multi-objective Biogeography-Based Optimization for Virtual Machine Placement, 2015 15th IEEE/ACM International Symposium on Cluster, Cloud and Grid Computing, pp.687-696, 2015.
DOI : 10.1109/CCGrid.2015.25