Low-latency intelligent service combination caching strategy with density peak clustering algorithm in vehicle edge computing

Abstract

In the dynamic field of Vehicle Edge Computing (VEC), the demand for intelligent vehicular systems to process vast amounts of data is escalating, driven by advancements in autonomous driving and real-time navigation technologies. Optimizing service latency and minimizing transmission costs are crucial for enhancing the performance of vehicular networks. Traditional service caching strategies, which largely rely on the popularity of individual services, often fail to account for the intricate interdependencies between services. The oversight can result in redundant data transfers and inefficient use of storage resources. In response, our paper introduces a novel approach to service combination caching within a heterogeneous computational framework comprising vehicles, edge servers, and the cloud. Our strategy focuses on minimizing user wait times and data transmission costs during task execution, while adhering to the caching budget constraints of service providers. Key contributions include the development of an Improved Density Peak Clustering (IDPC) algorithm to facilitate cooperative clustering among edge servers and the design of a Service Combination Caching Strategy (SCCS). The SCCS approach reduces caching costs by categorizing servers, forming efficient clusters, and strategically allocating storage. Simulation results demonstrate that the method outperforms existing strategies by significantly decreasing task execution delays and transmission costs, thereby greatly enhancing the quality of service in vehicular applications.