A Collaborative Caching and Offloading Approach for Vehicular Edge Computing

Vehicular Edge Computing (VEC) leverages promising technologies, namely the vehicle-to-vehicle (V2V) computation offloading approach and edge service caching, to address latency-sensitive tasks. The V2V offloading method efficiently harnesses idle resources from neighboring vehicles. Edge service caching facilitates the offloading task through pre-caching pertinent service data. However, formulating an efficient caching mechanism to support V2V offloading poses significant challenges, given the dynamic vehicle environment, varying computational resources, and limited caching resources of Roadside Units (RSUs). This paper introduces a collaborative caching and offloading (CACO) scheme. First, to mitigate resource wastage caused by inter-vehicle communication interruptions, we employ Generative Adversarial Network (GAN) for trajectory prediction. This process generates a relationship matrix, predicting the stability of inter-vehicle link connections to assist in V2V offloading decisions. Second, to circumvent redundant uploads and computations for recurring offloading tasks, we analyze the popularity of historical offloading tasks using the Page-Hinkley test (PHT) technique, caching frequently offloaded tasks to reduce the processing latency of offloading tasks. Subsequently, a matching scheme for caching and offloading contents is devised. Finally, the Deep Reinforcement Learning (DRL) algorithm is employed to train the offloading strategy. Results from extensive experiments substantiate that CACO attains superior performance in both system computational latency and offloading success rate.