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

Bomin Mao, Fengxiao Tang, Zubair Md Fadlullah, Nei Kato

Research output: Contribution to journalArticlepeer-review

46 Citations (Scopus)

Abstract

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.

Original languageEnglish
Pages (from-to)1554-1565
Number of pages12
JournalIEEE Transactions on Emerging Topics in Computing
Volume9
Issue number3
DOIs
Publication statusPublished - 2021

Keywords

  • Software defined communication systems
  • deep learning
  • real-time learning
  • routing computation

ASJC Scopus subject areas

  • Computer Science (miscellaneous)
  • Information Systems
  • Human-Computer Interaction
  • Computer Science Applications

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