부산대학교 도서관

The Computing First Network is a new type of information infrastructure that integrates various resources such as cloud, network, and edge, and performs unified scheduling of computing power at the network level according to business needs. When the scale of the computing power network is large, it may include multiple autonomous domains, and it is necessary to dispatch computing power equipment in other domains to meet the needs of the domain. In order to schedule computing power equipment more efficiently from a global perspective, multidomain scheduling of computing power is necessary. However, the problem of multi-domain scheduling of computing power equipment is more complicated. It is not easy to take care of multi-dimensional requirements simply by using protocols for scheduling. The existing machine learning models have poor generalization and poor performance when processing topologies that have not been seen in training. Simultaneously applied in domains with different topologies. In response to the above problems, this paper proposes a multi-domain computing power network path planning model based on the combination of Graph Neural Networks (GNN) and Deep Q Network (DQN). The model can handle unseen topologies well during the training process, and generate multiple alternative scheduling paths based on computing power requests and topology structures, and then use the decision-making ability of DQN to evaluate these paths to choose the most appropriate scheduling The path satisfies the computing power request. The experimental results show that, compared with the simple DQN model and Dijkstra algorithm, when the model proposed in this paper performs computing power scheduling, the task completion time is shorter and the network load is more balanced.