Intelligent Scheduling Strategy for Heterogeneous Multi-terminal Access Tasks for Complex Cloud-Edge Collaborative Computing

With the rapid development of network and mobile devices, the era of ten thousands households interconnection is gradually coming. The variety of applications is endless, putting higher demands on computing resources as well as latency. As a result, the task scheduling model of cloud-edge collaboration has emerged. In the current environment, due to the limited resources of edge nodes and the expanding volume of users and user data, how to allocate tasks to the best nodes becomes the most critical issue. To address this problem, this paper proposes the use of ant colony algorithm to optimize the cloud-edge collaborative task scheduling. The pheromone cumulative positive feedback method is mainly used to reflect the tendency of tasks to select processing devices. First, the probability of a processing device being selected by a task is calculated by pheromones. Then, a random number is used to determine the allocation strategy for each round of the cycle. Finally, the pheromone concentration of the processing device is updated and the minimum resource consumption as well as the optimal task assignment strategy is derived. The implementation results show that the ant colony algorithm can improve the response time, reduce the resource consumption, and improve the efficiency of multi-objective task scheduling.