An Intelligent Route Computation Approach Based on Real-Time Deep Learning Strategy for Software Defined Communication Systems

Software Defined Networking (SDN) is regarded as the next generation paradigm as it simplifies the structure of the data plane and improves the resource utilization. However, in current Software Defined Communication Systems (SDCSs), the maximum or minimum metric value based routing strategies come from traditional networks, which lack the ability of self-adaptation and do not efficiently utilize the computation resource in the controllers. To solve these problems, in this paper, we utilize the deep learning technique to conduct the routing computation for the SDCSs. Specifically, in our proposal, the considered Convolutional Neural Networks (CNNs) are adopted to intelligently compute the paths according to the input real-time traffic traces. To reduce the computation overhead of the central controller and improve the adaptation of CNNs to the changing traffic pattern, we consider an online training manner. Analysis shows that the computation complexity can be significantly reduced through the online training manner. Moreover, the simulation results demonstrate that our proposed CNNs are able to compute the appropriate paths combinations with high accuracy. Furthermore, the adopted periodical retraining enables the deep learning structures to adapt to the traffic changes.

Automated summary

In this paper, deep learning technique is utilized for routing computation in Software Defined Communication Systems, with Convolutional Neural Networks intelligently computing paths, online training reducing computation complexity, and periodical retraining enabling adaptation to traffic changes.