DeepMDR: A Deep-Learning-Assisted Control Plane System for Scalable, Protocol-Independent, and Multi-Domain Network Automation

Abstract

This article discusses DeepMDR, which is a deep learning (DL)-assisted control plane (CP) system to realize scalable and protocol-independent path computation in multi-domain packet networks. We develop DeepMDR based on ONOS, make it support protocol-oblivious forwarding (POF) in the data plane, facilitate a hierarchical CP architecture for multi-domain operations, and propose a DL model to achieve fast and high-quality path computation in each domain. Simulation results verify that our DL-assisted routing module achieves better trade-off between path computation time and routing performance than existing approaches. The effectiveness of our proposed DeepMDR is also demonstrated with experiments, which show that it serves inter-domain flow requests quickly with a processing capacity of ~166,000 messages/s or higher.