Alocação e posicionamento de recursos para redes de acesso virtualizadas com diferentes níveis de centralização
Abstract
There are great expectations on technologies such as network functions virtualization (NFV) and centralized or cloud radio access network (CRAN) and how they can accelerate deployment of new network services and at the same time decrease some costs of the network operators. In this context, there is a relevant problem that involves three main concerns: 1) which cell sites to be updated; 2) how to update the selected cell sites, i.e., change to fully virtual or not; and 3) where to serve the visualized cell sites. Those issues are influenced by the centralization level employed on a certain radio access network (RAN). We propose an optimization model that allows the decision maker to define the weight (or reward) of the centralization level and evaluate the impact on metrics such as the necessary investment and the achieved level of centralization. The model shows how the investment should be allocated depending on the level of centralization and the relative cost among the different resources. Our heuristic presents performance similar to the deterministic approach but it is able to obtain solutions much faster and to deal with large networks (i.e., cities and metropolitan regions).
References
Azarmand, Z. and Neishabouri, E. (2009). Location Allocation Problem. Physica-Verlag HD.
Basta, A., Kellerer, W., Hoffmann, M., Morper, H. J., and Hoffmann, K. (2014). Applying NFV and SDN to LTE Mobile Core Gateways, the Functions Placement Problem. In Proceedings of the 4th Workshop on All Things Cellular: Operations, Applications, and; Challenges, pages 33–38.
Baumgartner, A., Reddy, V. S., and Bauschert, T. (2015). Mobile core network virtualization: A model for combined virtual core network function placement and topology optimization. In Proceedings of the 2015 1st IEEE Conference on Network Softwarization (NetSoft), pages 1–9.
Bouet, M., Leguay, J., Combe, T., and Conan, V. (2015). Cost-based placement of International Journal of Network ManagevDPI functions in NFV infrastructures. ment, 25(6):490–506.
Checko, A., Christiansen, H. L., Yan, Y., Scolari, L., Kardaras, G., Berger, M. S., and Dittmann, L. (2015). Cloud RAN for Mobile Networks A Technology Overview. IEEE Communications Surveys Tutorials, 17(1):405–426.
Chih-Lin, Yuan, Y., Huang, J., Ma, S., Cui, C., and Duan, R. (2015). Rethink fronthaul for soft RAN. IEEE Communications Magazine, 53(9):82–88.
ElSawy, H., Hossain, E., and Haenggi, M. (2013). Stochastic Geometry for Modeling, Analysis, and Design of Multi-Tier and Cognitive Cellular Wireless Networks: A Survey. IEEE Communications Surveys & Tutorials, 15(3):996–1019.
ETSI (2014). Network Functions Virtualisation (NFV); Terminology for Main Concepts
in NFV. [link]. [Último acesso: 28-03-2017].
Garcia-Saavedra, A., Iosidis, G., Costa-Perez, X., and J.Leith, D. (2018). FluidRAN: Optimized vRAN/MEC Orchestration. In INFOCOM 2018.
Lee, D., Zhou, S., Zhong, X., Niu, Z., Zhou, X., and Zhang, H. (2014). Spatial modeling of the trafc density in cellular networks. Wireless Communications, IEEE, 21(1):80– 88.
Musumeci, F., Bellanzon, C., Carapellese, N., Tornatore, M., Pattavina, A., and Gosselin, S. (2016). Optimal BBU Placement for 5G C-RAN Deployment Over WDM Aggregation Networks. Journal of Lightwave Technology, 34(8):1963–1970.
Nunes, B., Mendonca, M., Nguyen, X. N., Obraczka, K., and Turletti, T. (2014). A Survey of Software-Dened Networking: Past, Present, and Future of Programmable Networks. IEEE Communications Surveys Tutorials, 16(3):1617–1634.
Telebrasil (2017). Telebrasil. http://www.telebrasil.org.br/panorama-do-setor/mapa-de-erbs-antenas/.
