用异步强化学习实现多边缘服务器协作，大幅降低延迟与能耗。
Cooperative Task Offloading through Asynchronous Deep Reinforcement Learning in Mobile Edge Computing for Future Networks
- 采用异步多智能体强化学习，实现边缘节点间协同任务卸载。
- 相比基线方案，系统延迟降低80%，能耗减少87%。
- 适合6G及未来物联网场景中高并发、低时延的计算需求。
未来网络（包括6G）将加速实现万物互联，但会带来对计算资源的极高需求。移动边缘计算（MEC）通过将计算密集型任务从终端设备卸载至附近边缘服务器，有效降低延迟和能耗。然而，单一MEC服务器在复杂场景下易导致资源分配不均，性能不佳；传统集中式卸载策略则存在传输延迟高、计算瓶颈明显的问题。为此，本文提出一种基于Transformer预测的协同任务卸载框架CTO-TP，利用异步多智能体深度强化学习，实现边缘节点间的协作，通过异步训练减少同步等待时间，优化分布式网络中的任务卸载与资源分配。性能评估表明，所提算法相比基线方案可降低80%的整体系统延迟和87%的能耗。
原文摘要 · Abstract (English)
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.
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