Resource Management at the Network Edge: A Deep Reinforcement Learning Approach

Abstract

With the advent of edge computing, it is highly recommended to extend some cloud services to the network edge such that the services can be provisioned in the proximity of end users, with better performance efficiency and cost efficiency. Compared to cloud computing, edge computing has high dynamics, and therefore the resources shall be correspondingly managed in an adaptive way. Traditional model-based resource management approaches are limited in practical application due to the involvement of some assumptions or prerequisites. We think it is desirable to introduce a model-free approach that can fit the network dynamics well without any prior knowledge. To this end, we introduce a model-free DRL approach to efficiently manage the resources at the network edge. Following the design principle of DRL, we design and implement a mobility-aware data processing service migration management agent. The experiments show that our agent can automatically learn the user mobility pattern and accordingly control the service migration among the edge servers to minimize the operational cost at runtime. Some potential future research challenges are also presented. © 1986-2012 IEEE.