Optimizing allocation and positioning in a disaggregated radio access network aware of paths through the core infrastructure

  • Felipe Freitas Fonseca Universidade Federal de Goiás
  • Sand Luz Correa Universidade Federal de Goiás-UFG
  • Kleber Vieira Cardoso Universidade Federal de Goiás

Resumo


Future wireless communication infrastructures, starting from 5G, will operate their radio access networks (RANs) based on virtualized functions distributed over a crosshaul, i.e., a transport solution integrating fronthaul and backhaul. Optimizing the resource allocation and positioning of the virtual network functions of a virtualized RAN (vRAN) is crucial to improve performance. In this paper, we propose a new optimization model to deal with vRAN functions allocation and positioning that seeks to maximize the level of centralization. Our model explores several representative functional splits, including the fully distributed remote unit (RU), while taking into account the limit imposed by the communication paths between the crosshaul and the core network. We compare our model with a state-of-the-art solution and show how our approach improves the centralization level in the majority of the scenarios, even considering the limit imposed by the core infrastructure. Our model also provides higher number of feasible solutions in most of the cases. Additionally, we investigate the positioning of the central unit (CU) and show that its colocation with the core infrastructure is rarely the best choice.

Palavras-chave: 5G, Gerenciamento de Infraestrutura, Alocação de Recursos

Referências

Asensio, A., Saengudomlert, P., Ruiz, M., and Velasco, L. (2016). Study of the centralization level of optical network-supported Cloud RAN. In 2016 International Conference on Optical Network Design and Modeling (ONDM), pages 1–6.

Chang, C., Nikaein, N., Knopp, R., Spyropoulos, T., and Kumar, S. S. (2017). FlexCRAN: A flexible functional split framework over ethernet fronthaul in Cloud-RAN. In 2017 IEEE International Conference on Communications (ICC), pages 1–7.

Chang, C., Nikaein, N., and Spyropoulos, T. (2016). Impact of Packetization and Scheduling on C-RAN Fronthaul Performance. In 2016 IEEE Global Communications Conference (GLOBECOM), pages 1–7.

Checko, A., Avramova, A. P., Berger, M. S., and Christiansen, H. L. (2016). Evaluating C-RAN fronthaul functional splits in terms of network level energy and cost savings. Journal of Communications and Networks, 18(2):162–172.

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.

Chen, X., Han, Z., Zhang, H., Xue, G., Xiao, Y., and Bennis, M. (2018). Wireless Resource Scheduling in Virtualized Radio Access Networks Using Stochastic Learning. IEEE Transactions on Mobile Computing, 17(4):961–974.

Costa-Perez, X., Garcia-Saavedra, A., Li, X., Deiss, T., de la Oliva, A., di Giglio, A., Iovanna, P., and Moored, A. (2017). 5G-Crosshaul: An SDN/NFV Integrated Fronthaul/ Backhaul Transport Network Architecture. IEEE Wireless Comm., 24(1):38–45.

de Lima, J. L. and Couto, R. S. (2018). Minimizac¸ ão da Latˆencia no Posicionamento de Func¸ ˜oes em Cloud RANs. Anais do Simp´osio Brasileiro de Redes de Computadores e Sistemas Distribu´ıdos (SBRC), 36:1–14.

de Souza, P. A., Abdallah, A. S., Bueno, E. F., and Cardoso, K. V. (2018). Virtualized Radio Access Networks: Centralization, Allocation, and Positioning of Resources. In 2018 IEEE International Conf. on Comm. Workshops (ICC Workshops), pages 1–6.

Desaulniers, G., Desrosiers, J., and Solomon, M. M. (2006). Column generation, volume 5. Springer Science & Business Media.

Garcia-Saavedra, A., Costa-Perez, X., Leith, D. J., and Iosifidis, G. (2018a). FluidRAN: Optimized vRAN/MEC Orchestration. In IEEE INFOCOM 2018 - IEEE Conference on Computer Communications, pages 2366–2374.

Garcia-Saavedra, A., Salvat, J. X., Li, X., and Costa-Perez, X. (2018b). WizHaul: On the Centralization Degree of Cloud RAN Next Generation Fronthaul. IEEE Transactions on Mobile Computing, 17(10):2452–2466.

Geoffrion, A. M. (1972). Generalized benders decomposition. Journal of optimization theory and applications, 10(4):237–260.

Lessmann, J. (2015). Resource optimization in realistic mobile backhaul networks. In 2015 IEEE International Conference on Communications (ICC), pages 3861–3866.

Lin, Y., Shao, L., Zhu, Z.,Wang, Q., and Sabhikhi, R. K. (2010). Wireless network cloud: Architecture and system requirements. IBM Journal of Research and Development, 54(1):4:1–4:12.

Rost, P., Bernardos, C. J., Domenico, A. D., Girolamo, M. D., Lalam, M., Maeder, A., Sabella, D., and W¨ubben, D. (2014). Cloud technologies for flexible 5G radio access networks. IEEE Communications Magazine, 52(5):68–76.

Rost, P., Talarico, S., and Valenti, M. C. (2015). The Complexity–Rate Tradeoff of Centralized Radio Access Networks. IEEE Transactions on Wireless Communications, 14(11):6164–6176.

Small Cell Forum (2016). Small cell virtualization functional splits and use cases. Document 159.07.02, Release 7.

Suryaprakash, V., Rost, P., and Fettweis, G. (2015). Are Heterogeneous Cloud-Based Radio Access Networks Cost Effective? IEEE Journal on Selected Areas in Communications, 33(10):2239–2251.

Mininet. http://www.mininet.org/. (visited on Mar. 10, 2018).
Publicado
27/08/2019
Como Citar

Selecione um Formato
FONSECA, Felipe Freitas; CORREA, Sand Luz; CARDOSO, Kleber Vieira. Optimizing allocation and positioning in a disaggregated radio access network aware of paths through the core infrastructure. In: SIMPÓSIO BRASILEIRO DE REDES DE COMPUTADORES E SISTEMAS DISTRIBUÍDOS (SBRC), 37. , 2019, Gramado. Anais do XXXVII Simpósio Brasileiro de Redes de Computadores e Sistemas Distribuídos. Porto Alegre: Sociedade Brasileira de Computação, aug. 2019 . p. 791-804. ISSN 2177-9384. DOI: https://doi.org/10.5753/sbrc.2019.7403.