Virtual Resource Allocation for URLLC in MEC-enabled UAVs: A Reliability and Availability Analysis
Abstract
Unmanned Aerial Vehicle (UAV) communication networks and Multiaccess Edge Computing (MEC) will occupy an important position in the future wireless communication system. Unlike regular datacenter environments, MEC can help mobile devices improve computing and communication capabilities, and its combination with UAVs helps to deal with the Line of Sight (LoS) issues, besides allowing node mobility. This work addresses the dynamic resource provisioning in a UAV equipped with MEC resources (MEC-enabled UAV) that provides on demand communication capabilities to Ultra-reliable and Low-latency Communication (URLLC) services. We adopt a Continuous Time Markov Chain (CTMC) to analyze the overall node availability and reliability, while taking into account virtual host setup (repair) delays and failure events for Virtual Network Functions (VNFs) hosted on MEC-enabled UAVs. Our results show that the containerized VNF setup delays critically impact the admission process, whereas reliability is more prone towards VNF failures.
References
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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.
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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.
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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.
Published
2022-05-23
How to Cite
FALCÃO, Marcos; SOUZA, Caio; BALIEIRO, Andson; DIAS, Kelvin.
Virtual Resource Allocation for URLLC in MEC-enabled UAVs: A Reliability and Availability Analysis. In: WORKSHOP ON MANAGEMENT AND OPERATION OF NETWORKS AND SERVICE (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.
