Task offloading optimization algorithm based on Lyapunov optimization

Aiming at the optimization problem of average delay and energy consumption caused by the dynamic arrival of tasks and the uncertainty of channel conditions in the process of task unloading in edge computing environment, a task offload optimization algorithm based on Lyapunov optimization is proposed. Firstly, the original problem is transformed into a deterministic optimization problem by using Lyapunov optimization method, and the task queue to be unloaded in the terminal equipment is transmitted to the cache base station according to priority. The queue length in the process of task unloading is constrained and modeled, which make queue length controllable to ensure system stability. Secondly, combined with the positive feedback mechanism of genetic algorithm, fast convergence and the advantages of fast global search ability and high precision efficiency of ant colony algorithm, the approximate optimal unloading path is found for the actual state of the cache base station with priority constraint queue, so as to the task can be offloaded to the appropriate mobile edge computing server efficiently. Finally, according to the constraints of the task queue and the optimization results of the offloading path, a heuristic global optimization task offloading algorithm is proposed. Compared with the existing EEDOA research methods through experimental simulation, the proposed algorithm can effectively reduce the unloading energy consumption of tasks by reasonably constraining the queue length and choosing the approximate optimal path.