An Energy-Efficient VNE Algorithm Based on Bidirectional Long Short-Term Memory

A bidirectional long short-term memory (Bi-LSTM) algorithm is proposed to resolve the problem of energy-efficient virtual network embedding. In the VNE process, a large number of attributes can provide information for efficient embedding. This paper divides them into three categories: “network characteristics”, “embedding sequence” and “task type”, and comprehensively analyzes their influence on the embedded performance of virtual networks. This study uses a graph convolutional network (GCN) to extract the network characteristics of virtual and substrate networks. By this approach, we embedded the network-topology graph containing nodes, links, and topological associations onto the input set of the GCN, which rapidly extracts network features. We then used the network features as the model input for the Bi-LSTM neural network to integrate historical and future embedding sequences into the training model. In this process, in conjunction with meta-reinforcement learning to accumulate the experience of various virtual-network tasks, we systematically adjusted model parameters and thus achieved the automatic tuning of neural networks. Simulation results show that when compared to similar existing algorithms. The proposed algorithm improves the acceptance ratio, average ratio of revenue and cost, and reduces the network energy consumption after integrating the network characteristics, embedding sequence, and task type.