DRJOA: intelligent resource management optimization through deep reinforcement learning approach in edge computing

Mobile edge computing (MEC) can enhance the computation capabilities of smart mobile devices for computation-intensive mobile applications via supporting computation offloading efficiently. However, the limitation of wireless resources and computational resources of edge servers often becomes the bottlenecks to realizing the developments of MEC. In order to address the computation offloading problem in the time-varying wireless networks, the offloading decisions and the allocation of radio and computation resources need to be jointly managed. Traditional optimization methods are challenging to deal with the combinatorial optimization problem in complex real-time dynamic network environments. Therefore, we propose a deep reinforcement learning (DRL)-based optimization approach, named DRJOA, which jointly optimizes offloading decisions, computation, and wireless resources allocation. The optimization algorithm based on DRL has the advantages of fast solving speed and strong generalization ability, which makes it possible to solve combinatorial optimization problems online. Simulation results show that our proposed DRJOA in this study dramatically outperforms the benchmark methods for offloading decisions and system utility.

Automated summary

Mobile edge computing (MEC) enhances computation capabilities of smart mobile devices for computation-intensive applications via efficient computation offloading. However, limitations in wireless and computational resources often hinder MEC development. To address offloading in time-varying wireless networks, offloading decisions and resource allocation must be jointly managed. Traditional optimization methods struggle with combinatorial optimization in dynamic network environments. Therefore, we propose a deep reinforcement learning (DRL)-based approach, named DRJOA, for optimizing offloading decisions, computation, and wireless resource allocation. Simulation results demonstrate that DRJOA outperforms benchmark methods in offloading decisions and system utility.