Dynamic Selection Slicing-Based Offloading Algorithm for In-Vehicle Tasks in Mobile Edge Computing

Abstract

With the surge in tasks for in-vehicle terminals, the resulting network congestion and time delay cannot meet the service needs of users. Offloading algorithms are introduced to handle vehicular tasks, which will greatly improve the above problems. In this paper, the dependencies of vehicular tasks are represented as directed acyclic graphs, and network slices are integrated within the edge server. The Dynamic Selection Slicing-based Offloading Algorithm for in-vehicle tasks in MEC (DSSO) is proposed. First, a computational offloading model for vehicular tasks is established based on available resources, wireless channel state, and vehicle loading level. Second, the solution of the model is transformed into a Markov decision process, and the combination of the DQN algorithm and Dueling Network from deep reinforcement learning is used to select the appropriate slices and dynamically update the optimal offloading strategy for in-vehicle tasks in the effective interval. Finally, an experimental environment is set up to compare the DSSO algorithm with LOCAL, MINCO, and DJROM, the results show that the system energy consumption of DSSO algorithm resources is reduced by 10.31%, the time latency is decreased by 22.75%, and the ratio of dropped tasks is decreased by 28.71%.