Adaptive Dynamic Service Placement Approach for Edge-Enabled Vehicular Networks Based on SAC and RF

Abstract

Edge computing offers crucial computational and storage support to vehicles by providing various services within the framework of the Internet of Vehicles in intelligent transportation systems. Service placement (SP) becomes particularly challenging when edge resources are limited and vehicles exhibit high-mobility. Many current dynamic placement methods rely on real-time placement, often leading to increased costs, instability, and frequent changes. This paper proposes SACRF-SP, an adaptive dynamic service placement algorithm based on Soft Actor-Critic (SAC) and Random Forest (RF), for dynamic urban traffic scenarios. This algorithm utilizes the SAC method to identify optimal placement nodes and integrates an RF model to predict service request trends. A decision network is constructed to assess the necessity of redeployment. Extensive simulation experiments demonstrate that SACRF-SP significantly reduces latency, resource usage, and the frequency of redeployment.