Cooperative Task Offloading Through Asynchronous Deep Reinforcement Learning in Mobile Edge Computing for Future Networks
Year: 2025
Type: conference-paper
Abstract: Future networks (including 6G) are poised to accelerate the realisation of Internet of Everything. The latter will imply a high demand for computational resources to support new services. Mobile Edge Computing (MEC) is a promising solution that enables offloading computation-intensive tasks to nearby edge servers from the end-user devices, thereby reducing latency and energy consumption. Nevertheless, relying solely on a single MEC server for task offloading can lead to uneven resource utilisation and suboptimal performance in complex scenarios. Additionally, traditional task offloading strategies specialise in centralised policy decisions, which unavoidably entails extreme transmission latency and reach computational bottleneck. To address these gaps, we propose a latency-efficient and energy-efficient Cooperative Task Offloading framework with Transformer-driven Prediction (CTO-TP), leveraging asynchronous multi-agent deep reinforcement learning to address these challenges. This approach fosters edge-edge cooperation and decreases the synchronous waiting time by performing asynchronous training, optimising task offloading, and resource allocation across distributed networks. The performance evaluation demonstrates that the proposed CTO-TP algorithm reduces up to 80% overall system latency and 87% energy consumption compared to the baseline schemes.
Authors: Yuelin Liu, Haiyuan Li, Xenofon Vasilakos, Rasheed Hussain, Dimitra Simeonidou
Institution: University of Bristol
Language: English
Topic: IoT and Edge/Fog Computing
Subfield: Computer Networks and Communications
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W7084157378