A Task Offloading and Content Caching Strategy for the Internet of Vehicles in Cloud-Edge Environment

A wide range of emerging in-vehicle applications can make the travel experience better for users. As the amount of vehicles on the road increases, so does the number of computational tasks that need to be processed, however, different vehicle users may request the same content, resulting in wasted resources. Therefore, IoV requires better compute offloading and content caching strategies to improve performance with respect to time latency and energy consumption. This paper proposes a joint task offloading and content caching optimization method based on forecasting traffic stream, called TOCC. First, temporal and spatial correlations are extracted from the preprocessed dataset using FOST and integrated to predict the traffic stream to obtain the number of tasks in the region at the next moment. To obtain a suitable joint optimization strategy for task offloading and content caching, the multi-objective problem of minimizing delay and energy consumption is decomposed into multiple single-objective problems using an improved MOEA/D via the Tchebycheff weight aggregation method, and a set of Pareto-optimal solutions is obtained. Finally, experimental results show the effectiveness of TOCC’s task offloading and task caching strategies and that TOCC outperforms than other methods with respect to time delay and energy consumption.