Edge-computing-based multi-objective equilibrium task unloading of Internet of Vehicles

The requirements of the Internet of Vehicles on the quality of service (QoS) of computing task offloading are constantly improving. The processing mode of edge computing, which deploys computing, bandwidth and storage resources near the user side, effectively improves the user experience. However, the computing capacity of edge servers is limited. Task offloading requests are overloaded and inefficient due to uneven server loads. Traditional cloud computing consumes a lot of energy and time. To solve the above problem, a KDQN based edge computing balanced task unloading mechanism is proposed: Q learning and convolutional neural network are used to obtain the optimal unloading strategy according to the returns of each step and continuous iterative updates, which reduces the convergence time of task unloading; K-means clustering is used to distribute part of the load of the overloaded server to the server with the closest European distance to achieve server load balancing. The experimental results show that compared to the existing offload mechanism, the new mechanism can reduce the average server energy consumption by 12.8%, the average service delay by 8.2%, and the average overload rate by 29.8%