Intelligent Joint Network Slicing and Routing via GCN-Powered Multi-Task Deep Reinforcement Learning

Abstract

In 6G mobile systems, network slicing is an emerging technology to support services with distinct requirements by dividing a common infrastructure into multiple logical networks. However, as a network management method, it is difficult for network slicing to achieve a real-time resource allocation to satisfy the stringent requirement of services in 6G networks. This paper introduces a joint network slicing and routing mechanism, which combines the network management and control framework to provide a fine-grained, real-time and dynamic resource allocation. Graph Convolutional Networks (GCN)-powered Multi-Task Deep Reinforcement Learning (DRL) is proposed to solve this complicated resource allocation problem. We first extend the DRL model into multi-task manner, where multiple output branches are matched to joint scheduling resources in every network slice. GCN with differentiable pooling mechanism is integrated into DRL model to capture the topological information from graph-structured network status. We implement our model in the SDN controller and evaluate it with representative topologies. The packet-level experiments show that 1) compared to rule-based and other learning-based methods, GCN-powered multi-task DRL can improve the performance of joint network slicing and routing; 2) our method is robust to diverse network environments; 3) in contrast with other learning-based algorithms, our method achieves a better performance.