References
- Bertsekas, D.P. (1987). Dynamic programming deterministic and stochastic models. Englewood Cliffs, NJ: Prentice-Hall.
- Buyya, R., Ranjan, R., & Calheiros, R. (2010). InterCloud: Utility-oriented federation of cloud computing environments for scaling of application services. In C.-H. Hsu L. Yang, J. Park, & S.-S. Yeo(Eds.), Algorithms and architectures for parallel processing SE - 2 (Vol. 6081, pp. 13–31). Berlin: Springer. http://doi.org/10.1007/978-3-642-13119-6_2
- Chaisiri, S., Lee, B.S., & Niyato, D. (2012). Optimization of resource provisioning cost in cloud computing. IEEE Transactions on Services Computing, 5(2), 164–177. http://doi.org/10.1109/TSC.2011.7
- CompatibleOne. (2015). CompatibleOne: The open source cloud broker. Retrieved May 20, 2001, fromhttp://www.compatibleone.org/
- Di Giorgio, A., Liberati, F., & Pietrabissa, A. (2013). On-board stochastic control of electric vehicle recharging. In T. Parisini, R. Tempo (Eds.), Proceedings of the 52nd IEEE Conference on Decision and Control (pp. 5710–5715). NJ, USA: IEEE. http://doi.org/ 10.1109/CDC.2013.6760789,
- FI-Core. (2015). (Future Internet – Core Platform), EU FP7-ICT Large-scale Integrating Project (IP), 2014- 2016. grant agreement no. 632893. Retrieved from http://cordis. europa.eu/project/rcn/192274_en.html
- FIWARE. (2014). (Future Internet Ware), EU FP7-ICT Large-scale Integrating Project (IP), 2011–2014. grant agreement no. 312826. Retrieved from http://www.fi-ware.eu/
- Gábor, Z., Kalmár, Z., & Szcpcsvári, C. (1998). Multi-criteria reinforcement learning. In Proceedings of the International Conference on Machine Learning (ICML 1998) (pp. 197–205). Madison, WI.
- Konstanteli, K., Cucinotta, T., Psychas, K., & Varvarigou, T. (2012). Admission control for elastic cloud services. In A. Zomaya, Y.-C. Chung (Eds.) Proceedings of the 2012 IEEE Fifth International Conference on Cloud Computing, Taipei (Taiwan) (pp. 41–48), NJ, USA: IEEE. http://doi.org/ 10.1109/CLOUD.2012.63
- Lewis, F., & Vrabie, D. (2009). Reinforcement learning and adaptive dynamic programming for feedback control. IEEE Circuits and Systems Magazine, 9(3), 32–50. http://doi.org/10.1109/MCAS.2009.933854
- Macone, D., Oddi, G., Palo, A., & Suraci, V. (2013). A dynamic load balancing algorithm for Quality of Service and mobility management in next generation home networks. Telecommunication Systems, 53(3), 265–283. http://doi.org/10.1007/s11235-013-9697-y
- Manfredi, S. (2010). A reliable cooperative and distributed management for wireless industrial monitoring and control. International Journal of Robust and Nonlinear Control, 20, 123–139. http://doi.org/10.1016/j.adhoc.2011.05.005
- Mascolo, S. (1999). Congestion control in high-speed communication networks using the Smith principle. Automatica, 35(12), 1921–1935. http://doi.org/10.1016/S0005-1098(99)00128-4
- McKeown, N., Anderson, T., Balakrishnan, H., Parulkar, G., Peterson, L., Rexford, J., … Turner, J. (2008). OpenFlow. ACM SIGCOMM Computer Communication Review, 38(2), 69–74. http://doi.org/10.1145/1355734.1355746
- National Institute of Standards and Technology. (2013). National Institute of Standards and Technology: Cloud Computing Program. Section 6.1. Retrieved March 20, 2003, from http://www.nist.gov/itl/cloud/6_1.cfm
- Oddi, G., Panfili, M., Pietrabissa, A., Zuccaro, L., & Suraci, V. (2013). A resource allocation algorithm of multi-cloud resources based on Markov decision process. In Diamond, S., Wainwright, N. (Eds.), Proceedings of the 2013 IEEE 5th International Conference on Cloud Computing Technology and Science (Vol. 1, pp. 130–135). NJ, USA: IEEE. http://doi.org/10.1109/CloudCom.2013.24
- Panfili, M., Pietrabissa, A., Oddi, G., & Suraci, V. (2016). A lexicographic approach to constrained MDP admission control. International Journal of Control, 89(2), 235–247. http://doi.org/10.1080/00207179.2015.1068955
- Pietrabissa, A. (2008a). Admission control in UMTS networks based on approximate dynamic programming. European Journal of Control, 14(1), 62–75. http://doi.org/10.3166/ejc.14.62-75
- Pietrabissa, A. (2008b). An alternative LP formulation of the admission control problem in multiclass networks. IEEE Transactions on Automatic Control, 53(3), 839–845. http://doi.org/10.1109/TAC.2008.919516
- Pietrabissa, A. (2009). A policy approximation method for the UMTS connection admission control problem modelled as an MDP. International Journal of Control, 82(10), 1814–1827. http://doi.org/10.1080/00207170902774233
- Pietrabissa, A. (2011). A reinforcement learning approach to call admission and call dropping control in links with variable capacity. European Journal of Control, 17(1), 89–103. http://doi.org/10.3166/ejc.17.89-103
- Pietrabissa, A., Battilotti, S., Facchinei, F., Giuseppi, A., Oddi, G., Panfili, M., & Suraci, V. (2015). Resource management in multi-cloud scenarios via reinforcement learning. In Xue A. (Ed.), Proceedings of the 2015 34th Chinese Control Conference (CCC) (Vol. 2015, pp. 9084–9089). NJ, USA: IEEE. http://doi.org/10.1109/ChiCC.2015.7261077
- PLATINO. (2015). Platform for innovative services in future internet, Italian Ministry of University and Research (MIUR) PLATINO project. grant agreement no. PON01_01007. Retrieved May 20, 2001, from https://sites.google.com/site/progettoplatino
- Puterman, M.L. (Ed.). (1994). Markov decision processes. New York, NY: John Wiley & Sons. http://doi.org/ 10.1002/9780470316887
- Rennie, J., & Mccallum, A.K. (1999). Using Reinforcement learning to spider the web efficiently. ICML, 99, 335–343.
- Sefraoui, O., Aissaoui, M., & Eleuldj, M. (2012). OpenStack: Toward an open-source solution for cloud computing. International Journal of Computer Applications, 55(3), 38–42. http://doi.org/10.5120/8738-2991
- Sirocco. (2015). Sirocco project: An open-source multi-cloud manager. Retrieved May 20, 2001, from http://sirocco.projects.ow2.org/xwiki/bin/view/Main
- Sutton, R.S., & Barto, A.G. (1998). Reinforcement learning: An introduction. Cambridge, MA: MIT Press.
- Tan, H., Balajee, K., & Lynn, D. (2014). Integration of evolutionary computing and reinforcement learning for robotic imitation learning. In Gruver, W. A., Chen, C. L. P. (Eds.), Proceedings of the 2014 IEEE International Conference on Systems, Man, and Cybernetics (SMC) (pp. 407–412). NJ, USA: IEEE. http://doi.org/10.1109/SMC.2014.6973941
- T-NOVA. (2015). Network Functions as-a-Service over Virtualised Infrastructures, EU FP7-ICT Large-scale Integrating Project (IP), 2014-2016. Retrieved May 20, 2011, from http://www.t-nova.eu/
- Woo, S.S., & Mirkovic, J. (2014). Optimal application allocation on multiple public clouds. Computer Networks, 68, 138–148. http://doi.org/10.1016/j.comnet.2013.12.001
- Wu, L., Kumar Garg, S., & Buyya, R. (2012). SLA-based admission control for a Software-as-a-Service provider in cloud computing environments. Journal of Computer and System Sciences, 78, 1280–1299. http://doi.org/10.1016/j.jcss.2011.12.014
- Xu, X., Zuo, L., & Huang, Z. (2014). Reinforcement learning algorithms with function approximation: Recent advances and applications. Information Sciences, 261, 1–31. http://doi.org/10.1016/j.ins.2013.08.037
- Yang, X., Liu, D., & Wang, D. (2014). Reinforcement learning for adaptive optimal control of unknown continuous-time nonlinear systems with input constraints. International Journal of Control, 87(3), 553–566. http://doi.org/10.1080/00207179.2013.848292