Distributed Multi-Cloud Multi-Access Edge Computing by Multi-Agent Reinforcement Learning

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.

Automated summary

This paper investigates a three-layer distributed multi-access edge computing network, where clouds, MEC servers, and edge devices are deployed at different layers to reduce system latency through task offloading and resource allocation. By proposing a distributed scheme based on reinforcement learning, a lower system latency is achieved compared to existing schemes, while also discussing the impact of resource competition and device numbers on performance.