In this paper, we consider a three-layer distributed multi-access edge computing (MEC) network where multiple clouds, MEC servers, and edge devices (EDs) are deployed at the top layer, middle layer, and bottom layer, respectively. Each cloud center (CC) is associated with an independent service provider and publishes an application-driven computing task. To deliver the tasks, CCs rely on EDs to generate the raw data and offload part of the computing tasks to both EDs and MEC servers such that their computing and transmission resources can be fully utilized to reduce the system latency. However, in such a three-layer network, the distributed deployment of tasks leads to inevitable resource competition among CCs. To address this issue, we propose a distributed scheme based on multi-agent reinforcement learning, where each CC jointly determines the task offloading and resource allocation strategy based on its inference of other CCs’ decisions. Simulation results indicate that a lower system latency is achieved via our proposed scheme compared with the existing schemes. In addition, the influence of the number of CCs, MEC servers, and EDs on latency performance is also discussed.

中文翻译：

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通过多智能体强化学习进行分布式多云多访问边缘计算

在本文中，我们考虑了一个三层分布式多访问边缘计算（MEC）网络，其中在顶层，中间层和底层分别部署了多个云，MEC服务器和边缘设备（ED）。每个云中心（CC）与一个独立的服务提供商关联，并发布应用程序驱动的计算任务。为了交付任务，CC依靠ED生成原始数据并将部分计算任务卸载到ED和MEC服务器，以便可以充分利用其计算和传输资源来减少系统延迟。但是，在这种三层网络中，任务的分布式部署导致CC之间不可避免的资源竞争。为了解决这个问题，我们提出了一种基于多主体强化学习的分布式方案，每个CC会根据对其他CC决策的推断来共同确定任务分流和资源分配策略。仿真结果表明，与现有方案相比，通过我们提出的方案可以实现更低的系统等待时间。此外，还讨论了CC，MEC服务器和ED数量对延迟性能的影响。