Resource Provisioning for URLLC and eMBB Services in MEC-NFV Networks: A CTMC-Based Analysis

  • Caio B. Bezerra De Souza UFPE
  • Marcos R. de Moraes Falcão UFPE
  • Maria G. Lima Damasceno Sidia / UFPE
  • Renata K. Gomes Dos Reis Sidia / UFPE
  • Andson M. Balieiro UFPE

Abstract


Multiple Access Edge Computing (MEC) and Network Function Virtualization (NFV) are key-technologies in the Fifth Generation of Mobile Networks (5G) to support services such as Ultra-Reliable and Low Latency Communication (URLLC) and Enhanced Mobile Broadband (eMBB). However, ensuring the coexistence of these services poses challenges, particularly in dynamic resource allocation within the MEC-NFV domain. This paper presents a Continuous Time Markov Chain (CTMC)based model to analyze the impact of dynamic resource allocation on both URLLC and eMBB services in an MEC-NFV environment. The analysis considers factors such as virtualization overhead, virtual resource failures, and varying numbers of containers and buffer sizes. The results indicate that availability, response time, and energy consumption are strongly influenced by the number of containers, while buffer size primarily affects response times.

References

3GPP (2020). System architecture for the 5g system (5gs). White Paper.

Abdelhadi, M., Sorour, S., ElSawy, H., Elsayed, S. A., and Hassanein, H. (2022). Parallel computing at the extreme edge: Spatiotemporal analysis. In GLOBECOM 2022 - 2022 IEEE Global Communications Conference, pages 5692–5698.

Bairagi, A. K., Munir, M. S., Alsenwi, M., Tran, N. H., Alshamrani, S. S., Masud, M., Han, Z., and Hong, C. S. (2021). Coexistence mechanism between embb and urllc in 5g wireless networks. IEEE Transactions on Communications, 69(3):1736–1749.

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. (2022). An analytical framework for urllc in hybrid mec environments. The Journal of Supercomputing, 78.

Falcao, M., Souza, C., Balieiro, A., and Dias, K. (2023). Dynamic resource allocation for urllc in uav-enabled multi-access edge computing. EuCNC 6G Summit.

Huang, H., Miao, W., Min, G., Tian, J., and Alamri, A. (2021). Nfv and blockchain enabled 5g for ultra-reliable and low-latency communications in industry: Architecture and performance evaluation. IEEE Transactions on Industrial Informatics, 17(8):5595–5604.

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.

Kim, Y. and Park, S. (2020). Calculation method of spectrum requirement for imt-2020 embb and urllc with puncturing based on m/g/1 priority queuing model. IEEE Access, 8:25027–25040.

Li, C., Cai, Q., Zhang, C., Ma, B., and Luo, Y. (2021). Computation offloading and service allocation in mobile edge computing. The Journal of Supercomputing, 77:1–30.

Li, W. and Jin, S. (2021). Performance evaluation and optimization of a task offloading strategy on the mobile edge computing with edge heterogeneity. The Journal of Super-computing, 77(8).

Liu, T., Fang, L., Zhu, Y., Tong, W., and Yang, Y. (2022). A near-optimal approach for online task offloading and resource allocation in edge-cloud orchestrated computing. IEEE Transactions on Mobile Computing, 21(8):2687–2700.

Raca, D., Leahy, D., Sreenan, C. J., and Quinlan, J. J. (2020). Beyond throughput, the next generation: A 5g dataset with channel and context metrics. In Proceedings of the 11th ACM Multimedia Systems Conference, MMSys ’20, page 303–308. Association for Computing Machinery.

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.

Setayesh, M. and Bahrami, S. (2022). Resource slicing for embb and urllc services in radio access network using hierarchical deep learning. volume 21.

Siddiqui, M. U. A., Abumarshoud, H., Bariah, L., Muhaidat, S., and Imran, Muhammad, L. (2023). Urllc in beyond 5g and 6g networks: An interference management perspective. IEEE Access, 11:54639–54663.

Souza, C., Falcao, M., Balieiro, A., and Dias, K. (2021). Modelling and analysis of 5g networks based on mec-nfv for urllc services. IEEE Latin America Transactions, 19(10):1745–1753.

Stallings, W. (2021). 5G Wireless: A Comprehensive Introduction. Addison-Wesley.

Tong, Z., Zhang, T., Zhu, Y., and Huang, R. (2020). Communication and computation resource allocation for end-to-end slicing in mobile networks. 2020 IEEE/CIC International Conference on Communications in China (ICCC), pages 1286–1291.

Zhang, T., Qiu, H., Linguaglossa, L., Cerroni, W., and Giaccone, P. (2021). Nfv platforms: Taxonomy, design choices and future challenges. IEEE Transactions on Network and Service Management, 18(1):30–48.
Published
2024-05-20
SOUZA, Caio B. Bezerra De; FALCÃO, Marcos R. de Moraes; DAMASCENO, Maria G. Lima; REIS, Renata K. Gomes Dos; BALIEIRO, Andson M.. Resource Provisioning for URLLC and eMBB Services in MEC-NFV Networks: A CTMC-Based Analysis. In: BRAZILIAN SYMPOSIUM ON COMPUTER NETWORKS AND DISTRIBUTED SYSTEMS (SBRC), 42. , 2024, Niterói/RJ. Anais [...]. Porto Alegre: Sociedade Brasileira de Computação, 2024 . p. 714-727. ISSN 2177-9384. DOI: https://doi.org/10.5753/sbrc.2024.1465.