Joint optimization of task caching and computation offloading in vehicular edge computing

The recent surge in the number of connected vehicles and vehicular applications really benefits citizens. Various vehicular applications are developed to cater for the increasingly sophisticated demands of drivers. Against this background, vehicular edge computing (VEC) is put forward as a promising solution to meet the strict latency requirement of these vehicular applications, by undertaking the computation offloaded from the nearby vehicles. Furthermore, task-oriented caching strategies are also applied to VEC for performance improvement. However, challenges faced by caching-enabled VEC still need to be addressed. For example, many factors can restrict the application of task caching in VEC, which usually include limited caching capability, extra energy consumption incurred by task caching, caching results delivery and so on. To overcome these issues, we propose a general caching-enabled VEC scheme and aim to jointly optimize the task caching and computation offloading in the VEC system. Moreover, we consider not only the response latency reduction benefitting from task caching, but also the energy consumption incurred by task caching. In particular, we strive to minimize the weighted sum of the service time and energy consumption for all the offloading requests in VEC. Due to the exponential time taken to obtain the optimal value, we in this paper propose a genetic algorithm-based task caching and computation offloading strategy. Extensive simulation has been carried out to investigate its efficiency compared to the benchmark algorithms. The simulation results reveal that the proposed strategy outperforms other approaches including the greedy approach and the random approach.