Decentralized Offloading Strategies Based on Reinforcement Learning for Multi-Access Edge Computing

Abstract

Using reinforcement learning technologies to learn offloading strategies for multi-access edge computing systems has been developed by researchers. However, large-scale systems are unsuitable for reinforcement learning, due to their huge state spaces and offloading behaviors. For this reason, this work introduces the centralized training and decentralized execution mechanism, designing a decentralized reinforcement learning model for multi-access edge computing systems. Considering a cloud server and several edge servers, we separate the training and execution in the reinforcement learning model. The execution happens in edge devices of the system, and edge servers need no communication. Conversely, the training process occurs at the cloud device, which causes a lower transmission latency. The developed method uses a deep deterministic policy gradient algorithm to optimize offloading strategies. The simulated experiment shows that our method can learn the offloading strategy for each edge device efficiently.

Trust‐aware task load balancing in multi‐access edge computing based on blockchain and a zero trust security capability framework

“…System components are virtualized and placed within a core network to provide a zero‐trust environment where only authenticated and authorized core network elements can access each other. In Reference 23, a centralized training and decentralized execution (CTDE) mechanism as a reinforcement balancing strategy was presented in the MEC environment. A decentralized multi‐agent reinforcement learning‐based balancing model was proposed to coordinate the trust relationships and update the sampling process.…”