Vehicle edge server deployment based on reinforcement learning in cloud-edge collaborative environment

Abstract

The rapid development of Internet of Vehicles (IoV) technology has led to a sharp increase in vehicle data. Traditional cloud computing is no longer sufficient to meet the high bandwidth and low latency requirements of IoV tasks. Ensuring the service quality of applications on in-vehicle devices has become challenging. Edge computing technology moves computing tasks from the cloud to edge servers with sufficient computing resources, effectively reducing network congestion and data propagation latency. The integration of edge computing and IoV technology is an effective approach to realizing intelligent applications in IoV.This paper investigates the deployment of vehicle edge servers in cloud-edge collaborative environment. Taking into consideration the vehicular mobility and the computational demands of IoV applications, the vehicular edge server deployment within the cloud-edge collaborative framework is formulated as a multi-objective optimization problem. This problem aims to achieve two primary objectives: minimizing service access latency and balancing server workload. To address this problem, a model is established for optimizing the deployment of vehicle edge servers and a deployment approach named VSPR is proposed. This method integrates hierarchical clustering and reinforcement learning techniques to effectively achieve the desired multi-objective optimization. Experiments are conducted using a real datasets from Shanghai Telecom to comprehensively evaluate the performance of workload balance and service access latency of vehicle edge servers under different deploy methods. Experimental results demonstrate that VSPR achieves an optimized balance between low latency and workload balancing while ensuring service quality, and outperforms SRL, CQP, K-means and Random algorithm by 4.76%, 44.59%, 40.78% and 69.33%, respectively.