Deterministic Task Offloading and Resource Allocation in the IoT-Edge-Cloud Continuum
K. Aghababaiyan, B. Coll-Perales, and J. Gozálvez
2025 IEEE 101th Vehicular Technology Conference (VTC2025-Spring)  (Oslo, Norway, June 2025)
Ed. IEEE  ISBN:979-8-3315-3147-8  ISSN:2577-2465  DOI:10.1109/VTC2025-Spring65109.2025.11174312  BIBTEX:@INPROCEEDINGS{11174312, author={Aghababaiyan, Keyvan and Coll-Perales, Baldomero and Gozalvez, Javier}, booktitle={2025 IEEE 101st Vehicular Technology Conference (VTC2025-Spring)}, title={Deterministic Task Offloading and Resource Allocation in the IoT-Edge-Cloud Continuum}, year={2025}, volume={}, number={}, pages={1-6}, keywords={Performance evaluation;Cellular networks;Vehicular and wireless technologies;Distributed processing;Scalability;Ecosystems;Resource management;Time factors;Internet of Things;Continuum;IoT-edge-cloud;deterministic;task offloading;resource allocation;subnetworks;IoT;critical}, doi={10.1109/VTC2025-Spring65109.2025.11174312}}

Resumen:





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.