Minimização da Latência no Posicionamento de Funções em Cloud RANs

• Jean Lucas de Lima UERJ
• Rodrigo S. Couto UERJ

DOI: https://doi.org/10.5753/sbrc.2018.2406

Abstract

The concept of Cloud Radio Access Network (C-RAN) is to perform radiobase functions in a cloud infrastructure, which can be centralized or composed of several hierarchical levels. Thus, the stations act only as signal receivers, which are later processed in the cloud. Given the distance between the cloud and the stations, latency is a critical factor in C-RAN. In this work, we formulate a mixed integer linear programming problem to choose the placement of the radio functions in a C-RAN, to minimize the latency in a cloud with different levels of hierarchy. To solve the problem, this work proposes two heuristics and shows situations in which they reach the optimal result.

References

Alyafawi, I., Schiller, E., Braun, T., Dimitrova, D., Gomes, A. e Nikaein, N. (2015). Critical issues of centralized and cloudied LTE-FDD radio access networks. Em IEEE ICC, p. 5523–5528.

Bartelt, J., Rost, P., Wubben, D., Lessmann, J., Melis, B. e Fettweis, G. (2015). Fronthaul and backhaul requirements of exibly centralized radio access networks. IEEE Wireless Communications, 22(5):105–111.

Checko, A., Christiansen, H. L., Yan, Y., Scolari, L., Kardaras, G., Berger, M. S. e DittIEEE mann, L. (2015). Cloud RAN for mobile networks-a technology overview. Communications surveys & tutorials, 17(1):405–426.

Coutinho, A. A. T. R., Carneiro, E. O. e Greve, F. G. P. (2016). Computação em névoa: Conceitos, aplicações e desaos. Em Minicursos do XXXIV SBRC, p. 266–315.

Dalla-Costa, A. G., Bondan, L., Wickboldt, J. A., Both, C. B. e Granville, L. Z. (2017a). Maestro: An NFV orchestrator for wireless environments aware of VNF internal compositions. Em IEEE AINA, p. 484–491.

Dalla-Costa, A. G., Schimuneck, M. A., Wickboldt, J. A., Both, C. B., Gaspary, L. P. e Granville, L. Z. (2017b). NFV em redes 5G: Avaliando o desempenho de composição de funções virtualizadas via Maestro. Em XXXV SBRC, p. 1–14.

GNU (2017). Glpk (GNU linear programming kit). https://www.gnu.org/software/glpk/. Acessado em dezembro de 2017.

Herrera, J. G. e Botero, J.-F. (2016). Resource allocation in NFV: A comprehensive survey.

IEEE Transactions on Network and Service Management, 13(3):518–532.

Luizelli, M. C., Bays, L. R., Buriol, L. S., Barcellos, M. P. e Gaspary, L. P. (2015). Piecing together the NFV provisioning puzzle: Ecient placement and chaining of virtual network functions. Em IFIP/IEEE IM, p. 98–106.

Mijumbi, R., Serrat, J., Gorricho, J.-L., Bouten, N., De Turck, F. e Boutaba, R. (2015). Network function virtualization: State-of-the-art and research challenges. IEEE Communications Surveys & Tutorials, 18(1):236–262.

Queiroz, G. F. C., Couto, R. S. e Sztajnberg, A. (2017). TRELIS: Posicionamento de funções virtuais de rede com economia de energia e resiliência. Em 16o WPERFORMANCE, p. 1656–1669.

Szwarcter, J. L. e Markenzon, L. (2013). Estruturas de Dados e seus Algoritmos. Livros Técnicos e Cientícos, 3 edição.

Wang, K., Zhao, M. e Zhou, W. (2014). Trac-aware graph-based dynamic frequency reuse for heterogeneous cloud-ran. Em IEEE GLOBECOM, p. 2308–2313.

Wubben, D., Rost, P., Bartelt, J. S., Lalam, M., Savin, V., Gorgoglione, M., Dekorsy, A. e Fettweis, G. (2014). Benets and impact of cloud computing on 5G signal processing: Flexible centralization through cloud-RAN. IEEE signal processing magazine, 31(6):35–44.