Simulation based QoS aware dynamic caching scheme for heterogeneous content requests in vehicular edge computing

Abstract

The Internet of Vehicles (IoV) has witnessed a substantial surge in its expansion during recent years, with the integration of edge servers emerging as an increasingly prevalent practice to cater to content requests that demand a superior quality of service. Nonetheless, conventional cache policies often prove inadequate for IoV applications, primarily due to their incapacity to effectively handle a diverse array of content requests, the high-speed mobility of vehicles, and the inherent instability of network connections. Within the scope of this study, we commence by bifurcating various content requests into two distinct categories: latency-sensitive content request and bandwidth-sensitive content request. Subsequently, we establish a model to evaluate the service quality under the joint consideration of different types of content requests, the mobility of vehicles and storage capacity of edge nodes. Furthermore, we transform the quality-of-service (QoS) penalty function into a system reward function. This adaption enables us to propose an innovative edge cache scheme founded upon the Deep Deterministic Policy Gradient (DDPG) algorithm of Reinforcement Learning (RL) method, which empowers dynamic adjustments in response to the evolving IoV environment. To validate the effectiveness of our proposed approach, we harness the Simulation of Urban Mobility (SUMO) traffic simulation software and construct a traffic road scenario based on a specific part segment of Nanjing Beltway. A wide-ranging set of contrast experiments was performed to ensure the improved performance of the DDPG based deep reinforcement learning method. Simulation experimental results show that the proposed algorithm converges quickly and outperforms existing algorithms in terms of service quality-hit ratio for latency-sensitive content and transmission speed for bandwidth-sensitive content.