Adaptive distributed cloud edge collaborative load control strategy for load management - LI Siwei; JIN Li; YU Long; DU Lishi; YUE Liang; ZHANG Xirun (Dianxin kexue)
To solve the problem that the controllable load management of multiple controllable load resources requires a lot of computing resources and can not achieve accurate automatic power control, a cloud-edge cooperative load resource allocation strategy for multiple controllable load regulation was proposed. Firstly, the collaborative control framework of cloud edge was designed to integrate and process the data of various controllable load resources. Secondly, considering the similarity of computing tasks of different edge nodes, the optimization goal was to minimize the time cost of all computing tasks, and the cloud computing resource allocation strategy was given to allocate computing resources reasonably. Finally, the computational resource allocation was solved by genetic algorithm based on adaptive cross-mutation probability. Finally, the calculation of resource allocation was solved using a genetic algorithm based on adaptive crossover mutation probability. The experimental results show that the algorithm proposed has significant advantages in task completion time and execution cost, and these advantages become more pronounced as the number of tasks increases and computing resources decrease. It can significantly improve computing efficiency and reduce computing time.