Enhanced routing using recurrent neural networks in software defined-data center network

Software Defined-DCN (SD-DCN) is a layered topology with logically centralized control which provides intelligence thro-ugh programmability. The controller in SD-DCN provides different network management skills by decoupling of control plane and data plane. However, the controller applies traditional artificial intelligence approaches and neural network approaches for routing strategies in recent systems. To manage dynamic changes in the network, deep learning approaches provide deep analysis and prediction to enhance the existing network performance. In convolutional neural network (CNN) deep learning model, the learning process executes based on present traffic data and forgets the inputs for the future learning phase. To overcome CNN limitation, in this article, different recurrent neural network (RNN) deep learning models are used to improve the routing computation for SD-DCN topology. The long short-term memory (LSTM)- RNN and bi-directional long short-term memory (BiLSTM)-RNN deep learning models provide forget gate to analyze periodic network traffic datasets and improve routing by delivering dynamic and intelligent routing paths in the network. The proposed system is implemented in a simulation scenario, and the proposed work improves the network performance in hot-spot traffic as compared to the existing mechanisms. Moreover, error analysis of different deep learning models is presented in the evaluation results.