Edge Computing and 5G URLLC Slicing for Industrial IoT: End-to-End Latency Decomposition across RAN, Transport, and Computer
DOI:
https://doi.org/10.63282/3117-5481/AIJCST-V7I4P110Keywords:
Industrial Iot, URLLC, End-To-End Latency, Network Slicing, Multi-Access Edge Computing, 5G, Latency Decomposition, RAN Scheduling, SDN-P4, Closed-Loop Control, High Performance ComputingAbstract
The deployment of fifth-generation networks as the communication backbone for industrial Internet of Things applications requiring sub-millisecond closed-loop control demands a systems engineering approach to latency management that treats the radio access network, fronthaul transport, multi-access edge compute, and application processing as jointly constrained components of a single end-to-end latency budget rather than independently optimized subsystems. This paper proposes a comprehensive end-to-end latency decomposition framework for industrial IoT ultra-reliable low-latency communication applications, partitioning the sub-millisecond latency budget across four domains and deriving the optimization constraints that each domain must satisfy for the system-level URLLC requirement to be achievable. The framework characterizes eight latency components spanning user equipment processing, radio access network scheduling, URLLC slice admission control, fronthaul propagation, multi-access edge computing queuing and processing, application logic execution, and actuation delay. For the radio access network slice management component, we build on the latency-aware SDN-P4 network slicing architecture of Prakhar et al., which demonstrated that programmable data plane-based admission control and in-band telemetry can enforce URLLC latency service-level agreements at the slice level, providing the sub-millisecond RAN contribution to the end-to-end budget. Experimental evaluation across six industrial IoT deployment scenarios, including computer numerical control machine tool control, autonomous mobile robot collision avoidance, and power grid protection relay, demonstrates that joint optimization across all four latency domains achieves the URLLC targets that domain-isolated optimization cannot satisfy, with an average 37 percent reduction in end-to-end latency violation rate relative to baseline architectures.
References
[1] 3GPP, "Study on NR ultra-reliable and low latency case," Technical Report TR 38.824, Release 15, 2019.
[2] P. Schulz, M. Matthe, H. Klessig, M. Simsek, G. Fettweis, J. Ansari, S. A. Ashraf, B. Almeroth, J. Voigt, I. Riedel, A. Puschmann, A. Mitschele-Thiel, M. Muller, T. Elste, and M. Windisch, "Latency critical IoT applications in 5G: Perspective on the design of radio interface and network architecture," IEEE Commun. Mag., vol. 55, no. 2, pp. 70-78, Feb. 2017.
[3] N. Varghese, R. L. Olsen, and H. P. Schwefel, "Latency requirements for industrial IoT use cases with 5G URLLC," in Proc. IEEE WFCS, 2020, pp. 1-8.
[4] P. Mach and Z. Becvar, "Mobile edge computing: A survey on the state of the art and future research topics," IEEE Commun. Surveys Tuts., vol. 19, no. 3, pp. 1628-1656, 3rd Qtr. 2017.
[5] J. Liu, Y. Mao, J. Zhang, and K. B. Letaief, "Delay-optimal computation task scheduling for mobile-edge computing systems," in Proc. IEEE ISIT, 2016, pp. 1451-1455.
[6] W. Shi, J. Cao, Q. Zhang, Y. Li, and L. Xu, "Edge computing: Vision and challenges," IEEE Internet Things J., vol. 3, no. 5, pp. 637-646, Oct. 2016.
[7] P. Popovski, J. J. Nielsen, C. Stefanovic, E. de Carvalho, E. Strom, K. F. Trillingsgaard, A.-S. Bana, D. M. Kim, R. Kotaba, J. Park, and R. B. Sorensen, "Wireless access for ultra-reliable low-latency communication: Principles and building blocks," IEEE Netw., vol. 32, no. 2, pp. 16-23, Mar. 2018.
[8] J. Mei, L. Zhao, K. Zheng, and X. Jiang, "Using stochastic network calculus to compute delay performance in 5G network slices," IEEE Access, vol. 7, pp. 42712-42722, 2019.
[9] A. Filali, Z. Mlika, S. Cherkaoui, and A. Kobbane, "Dynamic SDN-based radio access network slicing with deep reinforcement learning for URLLC and eMBB services," IEEE Trans. Cogn. Commun. Netw., vol. 7, no. 2, pp. 1-14, Jun. 2021.
[10] Prakhar, D. Upadhyay, M. Soni, S. Gupta, R. Sharma, and N. Venu, "Latency-Aware Network Slicing for 5G URLLC Applications: Design and Optimization Strategies," in Proc. 3rd IEEE Int. Conf. on Device Intelligence, Computing and Communication Technologies (DICCT 2025), pp. 113-118, 2025.
[11] A. Narayanan, E. Ramadan, J. Carpenter, Q. Liu, Y. Liu, F. Qian, and Z.-L. Zhang, "A first look at commercial 5G performance on smartphones," in Proc. ACM WWW, 2020, pp. 894-905.
[12] ETSI. Multi-access Edge Computing (MEC) Services for V2X Information Service. ETSI GS MEC 030, v2.1.1, 2020.
[13] Y. Polyanskiy, H. V. Poor, and S. Verdu, "Channel coding rate in the finite blocklength regime," IEEE Trans. Inf. Theory, vol. 56, no. 5, pp. 2307-2359, May 2010.
[14] X. Foukas, G. Patounas, A. Elmokashfi, and M. K. Marina, "Network slicing in 5G: Survey and challenges," IEEE Commun. Mag., vol. 55, no. 5, pp. 94-100, May 2017.
[15] M. Bennis, M. Debbah, and H. V. Poor, "Ultra-reliable and low-latency wireless communication: Tail, risk, and scale," Proc. IEEE, vol. 106, no. 10, pp. 1834-1853, Oct. 2018.
[16] C. Kim, T. Bhanu, H. T. Kim, and J. Lee, "In-band network telemetry: A survey," IEEE Commun. Surveys Tuts., vol. 24, no. 2, pp. 1238-1264, 2nd Qtr. 2022.
[17] K. Samdanis, X. Costa-Perez, and V. Sciancalepore, "From network sharing to multi-tenancy: The 5G network slice broker," IEEE Commun. Mag., vol. 54, no. 7, pp. 32-39, Jul. 2016.
[18] T. Li, A. K. Sahu, M. Zaheer, M. Sanjabi, A. Smola, and V. Smith, "Federated optimization in heterogeneous networks," in Proc. MLSys, 2020, pp. 429-450.
[19] S. Mao, P. Cheng, Y.-F. Liu, Y. Li, and B. Vucetic, "Joint task and bandwidth allocation for dynamic multi-slice radio access networks," IEEE Trans. Commun., vol. 69, no. 2, pp. 1232-1246, Feb. 2021.
[20] S. Gupta, "AI-Powered Optimization for High-Performance Computing in Scientific Simulations." Journal of Artificial Intelligence and Big Data 2024 4, no. 1: 1-8.
[21] 3GPP, "Management and orchestration of networks and network slicing," Technical Specification TS 28.531, Release 16, 2020.
