Published March 25, 2025 | Version pre-print
Conference paper Open

Deterministic Task Offloading and Resource Allocation in the IoT-Edge-Cloud Continuum

Description

Future cellular networks will sustainably integrate computing, intelligence and services within a “network of networks” ecosystem that includes IoT devices and subnetworks for local communications and distributed processing. This integration creates an IoT-edge-cloud continuum that enables opportunistic task offloading across the continuum, enhancing network performance, reducing response times and allowing a flexible resource allocation that can facilitate the system to scale according to demand. Future networks should also natively support deterministic service levels for critical and time-sensitive vertical applications. In this paper, we propose a deterministic task offloading and resource allocation scheme for the joint management of communication and computing resources in the IoT-edge-cloud continuum. The proposed scheme prioritizes task completion before deadlines over minimizing the latency in the execution of individual tasks. The scheme leverages flexible latencies across tasks to support a higher number of tasks through a more efficient management of computing and communication resources that better adapts to scenarios with constrained resources.

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Additional details

Funding

European Commission
6G-SHINE – 6G SHort range extreme communication IN Entitites 101095738
Ministerio de Ciencia, Innovación y Universidades
PID2020-115576RB-I00
Ministerio de Ciencia, Innovación y Universidades
PID2023-150308OB-I00

References

  • [1] U. Mikko, et al. "European Vision for the 6G Network Ecosystem", 6G-IA Vision Working Group' White Paper, Nov. 2024.
  • [2] G. P. Sharma et al., "Toward deterministic communications in 6G networks: state of the art, open challenges and the way forward," IEEE Access, vol. 11, pp. 106898–106923, 2023.
  • [3] J. Cai et al., "Multitask multiobjective deep reinforcement learning-based task offloading method for industrial Internet of Things," IEEE Internet Things J., vol. 10, no. 2, pp. 1848–1859, 2023.
  • [4] W. Fan et al., "Joint task offloading and resource allocation for multi-access edge computing assisted by parked and moving vehicles," IEEE Trans. Veh. Technol., vol. 71, no. 5, pp. 5314–5330, 2022.
  • [5] W. Fan et al., "Joint task offloading and resource allocation for vehicular edge computing based on V2I and V2V modes," IEEE Trans. Intell. Transp. Syst., vol. 24, no. 4, pp. 4277–4292, 2023.
  • [6] L. T. Oliveira et al., "Enhancing modular application placement in a hierarchical fog computing: A latency and communication cost-sensitive approach," Comput. Commun., vol. 216, pp. 95–111, 2024.
  • [7] K. Xiong et al., "Intelligent task offloading for heterogeneous V2X communications," IEEE Trans. Intell. Transp. Syst., vol. 22, no. 4, pp. 2226–2238, Apr. 2021.
  • [8] S. Xu et al., "RJCC: Reinforcement-learning-based joint communicational-and-computational resource allocation mechanism for smart city IoT," IEEE Internet Things J., vol. 7, no. 9, pp. 8059–8076, 2020.
  • [9] Z. Hong et al., "Multi-hop cooperative task offloading for industrial IoT–edge–cloud computing environments," IEEE Trans. Parallel Distrib. Syst., vol. 30, no. 12, pp. 2759–2774, 2019.
  • [10] W. Feng et al., "Latency minimization of reverse offloading in vehicular edge computing," IEEE Trans. Veh. Technol., vol. 71, no. 5, pp. 5343–5357, 2022.
  • [11] 6G-SHINE D2.2, "Refined definition of scenarios use cases and service requirements for in-X subnetworks," Feb. 2023.
  • [12] B. H. Arabi, "Solving NP-complete Problems Using Genetic Algorithms", in Proc. IEEE UKSim, pp. 43-48, Cambridge, UK, 2016.
  • [13] Intel, "Case Study of Scaled-Up SKT 5G MEC Reference Architecture", White Paper, 2022.
  • [14] 3GPP TS 36.211 v18.0.1 (2024), Evolved Universal Terrestrial Radio Access (E-UTRA); Physical Channels and Modulation (Release 18).
  • [15] Hu, M., et al. "Heterogeneous edge offloading with incomplete information: A minority game approach," IEEE Trans. Parallel Distrib. Syst. vol. 31, no. 9, pp. 2139-2154, 2020.