Multi-edge service cache scheduling method and system considering dynamic topology

• Main Authors: WEI ZHENCHUN, FAN YUQI, SHI LEI, ZHU CHENWEI, LV ZENGWEI, ZHANG BENHONG
• Format: Patent
• Language: Chinese; English
• Published: 20.01.2023
• Subjects: CALCULATING; COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS; COMPUTING; COUNTING; ELECTRIC COMMUNICATION TECHNIQUE; ELECTRICITY; PHYSICS; WIRELESS COMMUNICATIONS NETWORKS

The invention provides a multi-edge service cache scheduling method and system considering dynamic topology, and belongs to the technical field of edge networks. Based on the defect that a current multi-base-station edge caching method neglects the dynamic change of an edge server, a graph convolutional neural network and deep reinforcement learning are introduced into a multi-edge service caching problem, and the graph convolutional neural network can realize real-time information capture of dynamic topology; information subjected to feature extraction is transmitted to a deep reinforcement learning network for learning, and by combining the strong decision-making capability of deep reinforcement learning, cache decision can be well realized and service stability can be improved. 本发明提供了一种考虑动态拓扑的多边缘服务缓存调度方法和系统，属于边缘网络技术领域。本发明基于当前多基站的边缘缓存方法忽略了边缘服务器的动态变化的缺陷，在多边缘服务缓存问题中引入图卷积神经网络和深度强化学习，图卷积神经网络可实现对动态拓扑的实时信息捕捉，将经过特征提取后的信息传输到深度强化学习网络中进行学习，结合深度强化学习强大的决策能力，可以很好地实现缓存决策并提高服务稳定性。