Heurística Escalável Para o Problema de Alocação de vBBU e Comprimento de Onda em Cloud-Fog RAN
Resumo
O processamento de banda base centralizado impõe requisitos de alta largura de banda e atraso de fronthaul em implantações de Cloud Radio Access Networks (CRAN). Para resolver esses problemas, consideramos uma arquitetura híbrida chamada Cloud-Fog RAN (CF-RAN). Ele alivia as restrições de fronthaul usando computação em fog e virtualização de funções de rede (NFV) para colocar Unidades de processamento de banda base (BBUs) virtualizadas em nós de fog mais próximos dos usuários. Neste artigo, utilizamos programação linear inteira (PLI) e relaxação linear para resolver o problema da colocação de processamento de BBU virtual (vBBU) voltado para eficiência energética a partir da ativação mínima de elementos de processamento na rede. O principal objetivo deste artigo é apresentar uma alternativa escalável para a solução ótima com redução no tempo de execução. Os resultados mostram que uma abordagem heurística baseada no relaxamento linear reduz significativamente a utilização de recursos computacionais e o tempo de execução.
Referências
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Baruah, S. K., Bonifaci, V., Bruni, R., and Marchetti-Spaccamela, A. (2019). Ilp models for the allocation of recurrent workloads upon heterogeneous multiprocessors. Journal of Scheduling, 22(2):195-209.
Chadha, D. (2019). Optical WDM Networks: From Static to Elastic Networks. John Wiley & Sons.
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dos Santos, M. R., Tinini, R. I., Januario, T. O., and Figueiredo, G. B. (2022). Deep recurrent neural network for optical fronthaul dimensioning and proactive vbbu placement in cf-ran. Photonic Network Communications, 43(1):59-73.
Figueiredo, G. B., Wang, X., Meixner, C. C., Tornatore, M., and Mukherjee, B. (2016). Load balancing and latency reduction in multi-user comp over twdm-vpons. In IEEE Intl. Conf. on Communications (ICC).
Gao, Z., Zhang, J., Yan, S., Xiao, Y., Simeonidou, D., and Ji, Y. (2019). Deep reinforcement learning for bbu placement and routing in c-ran. In Optical Fiber Communications.
Gkatzios, N., Anastasopoulos, M., Tzanakaki, A., and Simeonidou, D. (2019). Efficiency gains in 5g softwarised radio access networks. EURASIP Journal on Wireless Communications and Networking, 2019(1):183.
IEEE (2019). Ieee draft standard for packet-based fronthaul transport networks. IEEE P1914.1/D5.0, April 2019, pages 1-89.
ITU, T. S. S. O. (2018). Series g: Transmission systems and media, digital systems and networks: 5g wireless fronthaul requirements in a passive optical network context. Technical report, ITU-T G-series Recommendations - Supplement 66.
Larsen, L. M. P., Checko, A., and Christiansen, H. L. (2019). A survey of the functional splits proposed for 5g mobile crosshaul networks. IEEE Communications Surveys Tutorials, 21(1):146-172.
Mo, W., Gutterman, C. L., Li, Y., Zussman, G., and Kilper, D. C. (2018). Deep neural network based dynamic resource reallocation of bbu pools in 5g c-ran roadm networks. In Optical Fiber Communications.
Mohammed Mikaeil, A., Hu, W., and Li, L. (2019). Joint allocation of radio and fronthaul resources in multi-wavelength-enabled c-ran based on reinforcement learning. Journal of Lightwave Technology.
Mukherjee, B. (2006). Optical WDM networks. Springer Science & Business Media.
Nassar, A. and Yilmaz, Y. (2019). Reinforcement learning for adaptive resource allocation in fog ran for iot with heterogeneous latency requirements. IEEE Access, 7:128014-128025.
Noor-E-Alam, M. and Doucette, J. (2012). Relax-and-fix decomposition technique for solving large scale grid-based location problems. Computers & Industrial Engineering, 63(4):1062-1073.
Peng, C., Lee, S.-B., Lu, S., Luo, H., and Li, H. (2011). Traffic-driven power saving in operational 3g cellular networks. In Proceedings of the 17th annual international conference on Mobile computing and networking, pages 121-132.
Rodoshi, R. T., Kim, T., and Choi, W. (2020). Resource management in cloud radio access network: Conventional and new approaches. Sensors, 20(9):2708.
Santos, M., Tinini, R., Januario, T., and Figueiredo, G. (2021a). Dimensionamento ótimo de fronthaul óptico com divisão flexível de funções de processamento em cfran. In Anais do XXXIX Simpósio Brasileiro de Redes de Computadores e Sistemas Distribuídos, pages 210-223, Porto Alegre, RS, Brasil. SBC.
Santos, M., Tinini, R., Januario, T., and Figueiredo, G. (2021b). Posicionamento quase ideal de vbbu e atribuição de comprimento de onda em cloud fog ran. In Anais do XXXIX Simpósio Brasileiro de Redes de Computadores e Sistemas Distribuídos, pages 448-461, Porto Alegre, RS, Brasil. SBC.
Tang, L., Zhang, X., Xiang, H., Sun, Y., and Peng, M. (2017). Joint resource allocation and caching placement for network slicing in fog radio access networks. In 2017 IEEE 18th International Workshop on Signal Processing Advances in Wireless Communications (SPAWC), pages 1-6.
Tinini, R. I., Batista, D. M., Figueiredo, G. B., Tornatore, M., and Mukherjee, B. (2019). Low-latency and energy-efficient bbu placement and vpon formation in virtualized cloud-fog ran. IEEE/OSA Journal of Optical Communications and Networking, 11(4):B37-B48.
Tinini, R. I., dos Santos, M. R. P., Figueiredo, G. B., and Batista, D. M. (2020). 5GPy: A SimPy-based simulator for performance evaluations in 5G hybrid Cloud-Fog RAN architectures. Simulation Modelling Practice and Theory, 101:102030.
Wu, J., Zhang, Z., Hong, Y., and Wen, Y. (2015). Cloud radio access network (c-ran): a primer. IEEE Network, 29(1):35-41.
Zaky Kasem, A., Doucette, J., et al. (2012). Ilp model and relaxation-based decomposition approach for incremental topology optimization in p-cycle networks. Journal of Computer Networks and Communications, 2012.
Zhang, J. and Jia, Z. (2022). Coherent passive optical networks for 100g/λ-and-beyond fiber access: Recent progress and outlook. IEEE Network, 36(2):116-123.
Baruah, S. K., Bonifaci, V., Bruni, R., and Marchetti-Spaccamela, A. (2019). Ilp models for the allocation of recurrent workloads upon heterogeneous multiprocessors. Journal of Scheduling, 22(2):195-209.
Chadha, D. (2019). Optical WDM Networks: From Static to Elastic Networks. John Wiley & Sons.
Consortium, G. et al. (2018). Ng-ran; architecture description. Technical report, Technical Report TR-38.401 Release 16.
dos Santos, M. R., Tinini, R. I., Januario, T. O., and Figueiredo, G. B. (2022). Deep recurrent neural network for optical fronthaul dimensioning and proactive vbbu placement in cf-ran. Photonic Network Communications, 43(1):59-73.
Figueiredo, G. B., Wang, X., Meixner, C. C., Tornatore, M., and Mukherjee, B. (2016). Load balancing and latency reduction in multi-user comp over twdm-vpons. In IEEE Intl. Conf. on Communications (ICC).
Gao, Z., Zhang, J., Yan, S., Xiao, Y., Simeonidou, D., and Ji, Y. (2019). Deep reinforcement learning for bbu placement and routing in c-ran. In Optical Fiber Communications.
Gkatzios, N., Anastasopoulos, M., Tzanakaki, A., and Simeonidou, D. (2019). Efficiency gains in 5g softwarised radio access networks. EURASIP Journal on Wireless Communications and Networking, 2019(1):183.
IEEE (2019). Ieee draft standard for packet-based fronthaul transport networks. IEEE P1914.1/D5.0, April 2019, pages 1-89.
ITU, T. S. S. O. (2018). Series g: Transmission systems and media, digital systems and networks: 5g wireless fronthaul requirements in a passive optical network context. Technical report, ITU-T G-series Recommendations - Supplement 66.
Larsen, L. M. P., Checko, A., and Christiansen, H. L. (2019). A survey of the functional splits proposed for 5g mobile crosshaul networks. IEEE Communications Surveys Tutorials, 21(1):146-172.
Mo, W., Gutterman, C. L., Li, Y., Zussman, G., and Kilper, D. C. (2018). Deep neural network based dynamic resource reallocation of bbu pools in 5g c-ran roadm networks. In Optical Fiber Communications.
Mohammed Mikaeil, A., Hu, W., and Li, L. (2019). Joint allocation of radio and fronthaul resources in multi-wavelength-enabled c-ran based on reinforcement learning. Journal of Lightwave Technology.
Mukherjee, B. (2006). Optical WDM networks. Springer Science & Business Media.
Nassar, A. and Yilmaz, Y. (2019). Reinforcement learning for adaptive resource allocation in fog ran for iot with heterogeneous latency requirements. IEEE Access, 7:128014-128025.
Noor-E-Alam, M. and Doucette, J. (2012). Relax-and-fix decomposition technique for solving large scale grid-based location problems. Computers & Industrial Engineering, 63(4):1062-1073.
Peng, C., Lee, S.-B., Lu, S., Luo, H., and Li, H. (2011). Traffic-driven power saving in operational 3g cellular networks. In Proceedings of the 17th annual international conference on Mobile computing and networking, pages 121-132.
Rodoshi, R. T., Kim, T., and Choi, W. (2020). Resource management in cloud radio access network: Conventional and new approaches. Sensors, 20(9):2708.
Santos, M., Tinini, R., Januario, T., and Figueiredo, G. (2021a). Dimensionamento ótimo de fronthaul óptico com divisão flexível de funções de processamento em cfran. In Anais do XXXIX Simpósio Brasileiro de Redes de Computadores e Sistemas Distribuídos, pages 210-223, Porto Alegre, RS, Brasil. SBC.
Santos, M., Tinini, R., Januario, T., and Figueiredo, G. (2021b). Posicionamento quase ideal de vbbu e atribuição de comprimento de onda em cloud fog ran. In Anais do XXXIX Simpósio Brasileiro de Redes de Computadores e Sistemas Distribuídos, pages 448-461, Porto Alegre, RS, Brasil. SBC.
Tang, L., Zhang, X., Xiang, H., Sun, Y., and Peng, M. (2017). Joint resource allocation and caching placement for network slicing in fog radio access networks. In 2017 IEEE 18th International Workshop on Signal Processing Advances in Wireless Communications (SPAWC), pages 1-6.
Tinini, R. I., Batista, D. M., Figueiredo, G. B., Tornatore, M., and Mukherjee, B. (2019). Low-latency and energy-efficient bbu placement and vpon formation in virtualized cloud-fog ran. IEEE/OSA Journal of Optical Communications and Networking, 11(4):B37-B48.
Tinini, R. I., dos Santos, M. R. P., Figueiredo, G. B., and Batista, D. M. (2020). 5GPy: A SimPy-based simulator for performance evaluations in 5G hybrid Cloud-Fog RAN architectures. Simulation Modelling Practice and Theory, 101:102030.
Wu, J., Zhang, Z., Hong, Y., and Wen, Y. (2015). Cloud radio access network (c-ran): a primer. IEEE Network, 29(1):35-41.
Zaky Kasem, A., Doucette, J., et al. (2012). Ilp model and relaxation-based decomposition approach for incremental topology optimization in p-cycle networks. Journal of Computer Networks and Communications, 2012.
Zhang, J. and Jia, Z. (2022). Coherent passive optical networks for 100g/λ-and-beyond fiber access: Recent progress and outlook. IEEE Network, 36(2):116-123.
Publicado
22/05/2023
Como Citar
SANTOS, Matias R. P. dos; TININI, Rodrigo I.; JANUARIO, Tiago; FIGUEIREDO, Gustavo B..
Heurística Escalável Para o Problema de Alocação de vBBU e Comprimento de Onda em Cloud-Fog RAN. In: SIMPÓSIO BRASILEIRO DE REDES DE COMPUTADORES E SISTEMAS DISTRIBUÍDOS (SBRC), 41. , 2023, Brasília/DF.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2023
.
p. 379-392.
ISSN 2177-9384.
DOI: https://doi.org/10.5753/sbrc.2023.420.
