Graph-Aware Deep Learning Based Intelligent Routing Strategy

• Published in: 2018 IEEE 43rd Conference on Local Computer Networks (LCN) pp. 441 - 444
• Main Authors: Zhuang, Zirui, Wang, Jingyu, Qi, Qi, Sun, Haifeng, Liao, Jianxin
• Format: Conference Proceeding
• Language: English
• Published: IEEE 01.10.2018
• Subjects: Computational modeling; Computer architecture; Control systems; Deep learning; Neural networks; Routing; Task analysis
• DOI: 10.1109/LCN.2018.8638099

Software defined networking decouples the control plane and data plane, which grants more computing power for routing computations. Traditional routing methods suffer from the complex dynamics in networking, and they are facing issues such as slow convergence and performance decline. Deep learning techniques have shown preliminary results on solving the routing problem, bring more accuracy and precision compared with traditional modeling techniques. However, the deep learning architecture needs to be specially customized to learn the topological relations between switches in an efficient way. Thus, we propose a deep learning based intelligent routing strategy with revised graph-aware neural networks and we design a set of features suitable for network routing. Then we demonstrate the performance of our works by using a real-world topology and the production level software switch. The simulation result shows our work is more accurate and efficient compared to state-of-art routing strategy.