Dual Dependency-Aware Collaborative Service Caching and Task Offloading in Vehicular Edge Computing

Abstract

Although some studies in recent years have focused on the coexistence of service and task dependencies in the collaborative optimization of service caching and task offloading in Vehicle Edge Computing (VEC), the challenges brought by dual dependencies have not been fully addressed. Therefore, this paper proposes a more comprehensive joint optimization method for service caching and task offloading under dual dependencies. First, this paper proposes a service criticality prediction method based on the Gated Graph Recurrent Network (GGRN) to perceive complex task dependencies and accurately capture the service requirements of critical task types. Based on this, a hierarchical active-passive hybrid caching strategy is designed, which aims to satisfy diverse service demands while reducing the additional overhead caused by remote service requests. Second, a global task priority computation method based on application heterogeneity has been developed to prevent cascading delays in task chains. Finally, this paper formulates a joint optimization problem for service caching and task offloading in a three-layer VEC system, models it as a markov decision process, and applies a proximal policy optimization-driven collaborative optimization algorithm named COHCTO. Simulation results show that COHCTO achieves multi-objective optimization across metrics such as delay, energy consumption, caching hit rate, and application success rate under conditions different from those of other algorithms.