Cooperative Task Offloading through Asynchronous Deep Reinforcement Learning in Mobile Edge Computing for Future Networks
This addresses latency and energy efficiency issues in MEC for future networks like 6G, but it is incremental as it builds on existing multi-agent deep reinforcement learning methods.
The paper tackles the problem of uneven resource utilization and suboptimal performance in Mobile Edge Computing (MEC) for future networks by proposing a cooperative task offloading framework, which reduces overall system latency by up to 80% and energy consumption by 87% compared to baselines.
Future networks (including 6G) are poised to accelerate the realisation of Internet of Everything. However, it will result in a high demand for computing resources to support new services. Mobile Edge Computing (MEC) is a promising solution, enabling to offload computation-intensive tasks to nearby edge servers from the end-user devices, thereby reducing latency and energy consumption. However, 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 entail extreme transmission latency and reach computational bottleneck. To fill the gaps, we propose a latency 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.