Virtual Resource Allocation for URLLC in MEC-enabled UAVs: A Reliability and Availability Analysis
Resumo
Redes de comunicação de veículos aéreos não tripulados (UAV) e computação de borda de acesso múltilplo (MEC) ocuparão uma posição importante no sistema de comunicação sem fio do futuro. Diferentemente dos ambientes comuns de datacenter, o MEC pode ajudar os dispositivos móveis a melhorar as capacidades de computação e comunicação, e sua combinação com UAVs ajuda a lidar com os problemas de Linha de Visão (LoS), além de permitir a mobilidade dos nós. Este artigo aborda o provisionamento dinâmico de recursos de um UAV equipado com uma nuvem MEC que fornece capacidades de comunicação/processamento sob demanda para serviços de confiabilidade alta e latência baixa (URLLC). Nós analisamos a disponibilidade e confiabilidade dos nós via cadeia de markov de tempo contínuo (CMTC), considerando o atraso de incialização/reparo e eventos de falha de funções de redes virtuais (VNFs) embarcadas em UAVs com MEC. Nossos resultados mostram que os atrasos de configuração de VNF em contêineres impactam criticamente o processo de admissão, enquanto a confiabilidade é mais afetada pelas falhas.
Referências
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Borja, N., Sanchez-Aguero, V., Vidal, I., and Valera, F. (2018). Adaptable and automated small uav deployments via virtualization. Sensors, 18:4116.
Chen, Z., Xiao, N., and Han, D. (2020). Multilevel task offloading and resource optimization of edge computing networks considering uav relay and green energy. Journal of Applied Sciences, 10:2592-2601.
Cheng, N., Xu, W., Shi, W., Zhou, Y., Lu, N., Zhou, H., and Shen, X. (2018). Air-ground integrated mobile edge networks: Architecture, challenges, and opportunities. IEEE Communications Magazine, 56(8):26-32.
Costanzo, F., Lorenzo, P. D., and Barbarossa, S. (2020). Dynamic resource optimization and altitude selection in uav-based multi-access edge computing. In ICASSP 2020 2020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pages 4985-4989.
Emara, M., ElSawy, H., Filippou, M. C., and Bauch, G. (2021). Spatiotemporal dependable task execution services in mec-enabled wireless systems. IEEE Wireless Communications Letters, 10(2):211-215.
Falcao, M., Souza, C., Balieiro, A., and Dias, K. (2021). An analytical framework for urllc in hybrid mec environments. Journal of Supercomputing.
Fautrel, T., George, L., Fauberteau, F., and Grandpierre, T. (2019). An hypervisor approach for mixed critical real-time uav applications. In 2019 IEEE International Conference on Pervasive Computing and Communications Workshops (PerCom Workshops), pages 985-991.
Hu, Q., Cai, Y., Yu, G., Qin, Z., Zhao, M., and Li, G. Y. (2019). Joint offloading and trajectory design for uav-enabled mobile edge computing systems. IEEE Internet of Things Journal, 6(2):1879-1892.
Jeong, S., Simeone, O., and Kang, J. (2018). Mobile edge computing via a uav-mounted cloudlet: Optimization of bit allocation and path planning. IEEE Transactions on Vehicular Technology, 67(3):2049-2063.
Kaur, K., Dhand, T., Kumar, N., and Zeadally, S. (2017). Container-as-a-service at the edge: Trade-off between energy efficiency and service availability at fog nano data centers. IEEE Wireless Communications, 24(3):48-56.
Lal, S., Ravidas, S., Oliver, I., and Taleb, T. (2017). Assuring virtual network function image integrity and host sealing in telco cloue. In 2017 IEEE International Conference on Communications (ICC), pages 1-6.
Li, W.-T., Zhao, M., Wu, Y.-H., Yu, J.-J., Bao, L.-Y., Yang, H., and Liu, D. (2021). Collaborative offloading for uav-enabled time-sensitive mec networks. EURASIP Journal on Wireless Communications and Networking.
Li, X., Yao, H., Wang, J., Xu, X., Jiang, C., and Hanzo, L. (2019). A near-optimal uav-aided radio coverage strategy for dense urban areas. IEEE Transactions on Vehicular Technology, 68(9):9098-9109.
Lyu, J., Zeng, Y., Zhang, R., and Lim, T. J. (2017). Placement optimization of uav-mounted mobile base stations. IEEE Communications Letters, 21(3):604-607.
Morabito, R. (2015). Power consumption of virtualization technologies: An empirical investigation. In 2015 IEEE/ACM 8th International Conference on Utility and Cloud Computing (UCC), pages 522-527.
Mozaffari, M., Taleb Zadeh Kasgari, A., Saad, W., Bennis, M., and Debbah, M. (2019). Beyond 5g with uavs: Foundations of a 3d wireless cellular network. IEEE Transactions on Wireless Communications, 18(1):357-372.
Nasir, A. A., Tuan, H. D., Duong, T. Q., and Poor, H. V. (2019). Uav-enabled communication using noma. IEEE Transactions on Communications, 67(7):5126-5138.
Nielsen, J. J., Liu, R., and Popovski, P. (2018). Ultra-reliable low latency communication using interface diversity. IEEE Transactions on Communications, 66(3):1322-1334.
Ren, Y., Phung-Duc, T., Yu, Z.-W., and Chen, J.-C. (2016). Design and analysis dynamic auto scaling algorithm (dasa) for 5g mobile networks.
Santoyo-González, A. and Cervelló-Pastor, C. (2018). Edge nodes infrastructure placement parameters for 5g networks. In 2018 IEEE Conference on Standards for Communications and Networking (CSCN), pages 1-6.
Sarrigiannis, I., Ramantas, K., Kartsakli, E., Mekikis, P.-V., Antonopoulos, A., and Verikoukis, C. (2020). Online vnf lifecycle management in an mec-enabled 5g iot architecture. IEEE Internet of Things Journal, 7(5):4183-4194.
Strinati, E. C., Barbarossa, S., Choi, T., A.Pietrabissa, Giuseppi, A., Santis, E. D., Vidal, J., Becvar, Z., Haustein, T., N.Cassiau, Costanzo, F., Kim, J., and Kim, I. (2020). 6g in the sky: On-demand intelligence at the edge of 3d networks. ETRI Journal, 42(5):643-657.
Wang, L., Che, Y. L., Long, J., Duan, L., and Wu, K. (2019). Multiple access mmwave design for uav-aided 5g communications. IEEE Wireless Communications, 26(1):64-71.
Wang, L., Wang, K., Pan, C., Xu, W., Aslam, N., and Hanzo, L. (2021). Multi-agent deep reinforcement learning-based trajectory planning for multiuav assisted mobile edge computing. IEEE Transactions on Cognitive Communications and Networking, 7(1):73-84.
Xiong, J., Guo, H., and Liu, J. (2019). Task offloading in uav-aided edge computing: Bit allocation and trajectory optimization. IEEE Communications Letters, 23(3):538-541.
Yang, Z., Pan, C., Wang, K., and Shikh-Bahaei, M. (2019). Energy efficient resource allocation in uav-enabled mobile edge computing networks. IEEE Transactions on Wireless Communications, 18(9):4576-4589.
Zeng, Y., Wu, Q., and Zhang, R. (2019). Accessing from the sky: A tutorial on uav communications for 5g and beyond. Proceedings of the IEEE, 107(12):2327-2375.
Zhang, J., Zhou, L., Tang, Q., Ngai, E. C.-H., Hu, X., Zhao, H., and Wei, J. (2019). Stochastic computation offloading and trajectory scheduling for uavassisted mobile edge computing. IEEE Internet of Things Journal, 6(2):3688-3699.
Anand, A. and de Veciana, G. (2018). Resource allocation and harq optimization for urllc traffic in 5g wireless networks. IEEE Journal on Selected Areas in Communications, 36(11):2411-2421.
Bekkouche, O., Samdanis, K., Bagaa, M., and Taleb, T. (2020). A service-based architecture for enabling uav enhanced network services. IEEE Network, 34(4):328-335.
Borja, N., Sanchez-Aguero, V., Vidal, I., and Valera, F. (2018). Adaptable and automated small uav deployments via virtualization. Sensors, 18:4116.
Chen, Z., Xiao, N., and Han, D. (2020). Multilevel task offloading and resource optimization of edge computing networks considering uav relay and green energy. Journal of Applied Sciences, 10:2592-2601.
Cheng, N., Xu, W., Shi, W., Zhou, Y., Lu, N., Zhou, H., and Shen, X. (2018). Air-ground integrated mobile edge networks: Architecture, challenges, and opportunities. IEEE Communications Magazine, 56(8):26-32.
Costanzo, F., Lorenzo, P. D., and Barbarossa, S. (2020). Dynamic resource optimization and altitude selection in uav-based multi-access edge computing. In ICASSP 2020 2020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pages 4985-4989.
Emara, M., ElSawy, H., Filippou, M. C., and Bauch, G. (2021). Spatiotemporal dependable task execution services in mec-enabled wireless systems. IEEE Wireless Communications Letters, 10(2):211-215.
Falcao, M., Souza, C., Balieiro, A., and Dias, K. (2021). An analytical framework for urllc in hybrid mec environments. Journal of Supercomputing.
Fautrel, T., George, L., Fauberteau, F., and Grandpierre, T. (2019). An hypervisor approach for mixed critical real-time uav applications. In 2019 IEEE International Conference on Pervasive Computing and Communications Workshops (PerCom Workshops), pages 985-991.
Hu, Q., Cai, Y., Yu, G., Qin, Z., Zhao, M., and Li, G. Y. (2019). Joint offloading and trajectory design for uav-enabled mobile edge computing systems. IEEE Internet of Things Journal, 6(2):1879-1892.
Jeong, S., Simeone, O., and Kang, J. (2018). Mobile edge computing via a uav-mounted cloudlet: Optimization of bit allocation and path planning. IEEE Transactions on Vehicular Technology, 67(3):2049-2063.
Kaur, K., Dhand, T., Kumar, N., and Zeadally, S. (2017). Container-as-a-service at the edge: Trade-off between energy efficiency and service availability at fog nano data centers. IEEE Wireless Communications, 24(3):48-56.
Lal, S., Ravidas, S., Oliver, I., and Taleb, T. (2017). Assuring virtual network function image integrity and host sealing in telco cloue. In 2017 IEEE International Conference on Communications (ICC), pages 1-6.
Li, W.-T., Zhao, M., Wu, Y.-H., Yu, J.-J., Bao, L.-Y., Yang, H., and Liu, D. (2021). Collaborative offloading for uav-enabled time-sensitive mec networks. EURASIP Journal on Wireless Communications and Networking.
Li, X., Yao, H., Wang, J., Xu, X., Jiang, C., and Hanzo, L. (2019). A near-optimal uav-aided radio coverage strategy for dense urban areas. IEEE Transactions on Vehicular Technology, 68(9):9098-9109.
Lyu, J., Zeng, Y., Zhang, R., and Lim, T. J. (2017). Placement optimization of uav-mounted mobile base stations. IEEE Communications Letters, 21(3):604-607.
Morabito, R. (2015). Power consumption of virtualization technologies: An empirical investigation. In 2015 IEEE/ACM 8th International Conference on Utility and Cloud Computing (UCC), pages 522-527.
Mozaffari, M., Taleb Zadeh Kasgari, A., Saad, W., Bennis, M., and Debbah, M. (2019). Beyond 5g with uavs: Foundations of a 3d wireless cellular network. IEEE Transactions on Wireless Communications, 18(1):357-372.
Nasir, A. A., Tuan, H. D., Duong, T. Q., and Poor, H. V. (2019). Uav-enabled communication using noma. IEEE Transactions on Communications, 67(7):5126-5138.
Nielsen, J. J., Liu, R., and Popovski, P. (2018). Ultra-reliable low latency communication using interface diversity. IEEE Transactions on Communications, 66(3):1322-1334.
Ren, Y., Phung-Duc, T., Yu, Z.-W., and Chen, J.-C. (2016). Design and analysis dynamic auto scaling algorithm (dasa) for 5g mobile networks.
Santoyo-González, A. and Cervelló-Pastor, C. (2018). Edge nodes infrastructure placement parameters for 5g networks. In 2018 IEEE Conference on Standards for Communications and Networking (CSCN), pages 1-6.
Sarrigiannis, I., Ramantas, K., Kartsakli, E., Mekikis, P.-V., Antonopoulos, A., and Verikoukis, C. (2020). Online vnf lifecycle management in an mec-enabled 5g iot architecture. IEEE Internet of Things Journal, 7(5):4183-4194.
Strinati, E. C., Barbarossa, S., Choi, T., A.Pietrabissa, Giuseppi, A., Santis, E. D., Vidal, J., Becvar, Z., Haustein, T., N.Cassiau, Costanzo, F., Kim, J., and Kim, I. (2020). 6g in the sky: On-demand intelligence at the edge of 3d networks. ETRI Journal, 42(5):643-657.
Wang, L., Che, Y. L., Long, J., Duan, L., and Wu, K. (2019). Multiple access mmwave design for uav-aided 5g communications. IEEE Wireless Communications, 26(1):64-71.
Wang, L., Wang, K., Pan, C., Xu, W., Aslam, N., and Hanzo, L. (2021). Multi-agent deep reinforcement learning-based trajectory planning for multiuav assisted mobile edge computing. IEEE Transactions on Cognitive Communications and Networking, 7(1):73-84.
Xiong, J., Guo, H., and Liu, J. (2019). Task offloading in uav-aided edge computing: Bit allocation and trajectory optimization. IEEE Communications Letters, 23(3):538-541.
Yang, Z., Pan, C., Wang, K., and Shikh-Bahaei, M. (2019). Energy efficient resource allocation in uav-enabled mobile edge computing networks. IEEE Transactions on Wireless Communications, 18(9):4576-4589.
Zeng, Y., Wu, Q., and Zhang, R. (2019). Accessing from the sky: A tutorial on uav communications for 5g and beyond. Proceedings of the IEEE, 107(12):2327-2375.
Zhang, J., Zhou, L., Tang, Q., Ngai, E. C.-H., Hu, X., Zhao, H., and Wei, J. (2019). Stochastic computation offloading and trajectory scheduling for uavassisted mobile edge computing. IEEE Internet of Things Journal, 6(2):3688-3699.
Publicado
23/05/2022
Como Citar
FALCÃO, Marcos; SOUZA, Caio; BALIEIRO, Andson; DIAS, Kelvin.
Virtual Resource Allocation for URLLC in MEC-enabled UAVs: A Reliability and Availability Analysis. In: WORKSHOP DE GERÊNCIA E OPERAÇÃO DE REDES E SERVIÇOS (WGRS), 27. , 2022, Fortaleza.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2022
.
p. 43-56.
ISSN 2595-2722.
DOI: https://doi.org/10.5753/wgrs.2022.223477.
