An Adaptive Alternating Direction Method of Multipliers for Vehicle-to-Everything Computation Offloading in Cloud-Edge Collaborative Environment

Vehicle-to-Everything edge computing accomplishes the goal of low latency by offloading tasks to edge computing servers, but how to reduce the computing latency of vehicle terminals while ensuring low energy consumption and load balance of servers is still a challenge. In order to address this issue, this paper proposes an adaptive computation offloading strategy based on Adaptive Alternating Direction Method of Multipliers (AADMM). Firstly, a distributed framework for multiple vehicles and multiple Road Side Units(RSUs) is constructed by comprehensively considering the weights of delay and energy consumption, with the optimization objective of minimizing the total system cost. Secondly, the original variables and dual variables are updated alternately, and the step size is dynamically adjusted based on the magnitude of variable updates, thereby progressively approaching the optimal solution. Finally, simulation experiments show that our proposed strategy can effectively reduce the system cost compared with other traditional algorithms under the comprehensive consideration of delay and energy consumption, and our proposed algorithm has better performance in terms of number of vehicles, speed of vehicles, size of the task, etc.