Deep Reinforcement Learning-Based Resource Allocation for Content Distribution in IoT-Edge-Cloud Computing Environments

Abstract

With the emergence of intelligent terminals, the Internet of Vehicles (IoV) has been drawing great attention by taking advantage of mobile communication technologies. However, high computation complexity, collaboration communication overhead and limited network bandwidths bring severe challenges to the provision of latency-sensitive IoV services. To overcome these problems, we design a cloud-edge cooperative content-delivery strategy in asymmetrical IoV environments to minimize network latency by providing optimal computing, caching and communication resource allocation. We abstract the joint allocation issue of heterogeneous resources as a queuing theory-based latency minimization objective. Next, a new deep reinforcement learning (DRL) scheme works in each network node to achieve optimal content caching and request routing on the basis of the perceptive request history and network state. Extensive simulations show that our proposed strategy has lower network latency compared with the current solutions in the cloud-edge collaboration system and converges fast under different scenarios.

Keywords

• cloud-edge cooperation
• queuing theory
• deep reinforcement learning
• in-networking caching
• content popularity