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Machine-Learning Based Routing Pre-plan for SDN

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Multi-disciplinary Trends in Artificial Intelligence (MIWAI 2015)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 9426))

Abstract

In Software Defined Network (SDN) environment, controller has to compute and install routing strategy for each new flow, leading to a lot of computation and communication burden in both controller and data planes. In this background, intelligent routing pre-design mechanism is regarded to be an important approach for routing efficiency enhancement. This paper investigates and proposes efficient SDN routing pre-design solution in three aspects: flow feature extraction, requirement prediction and route selection. First, we analyze and extract data packet and association features from user history data, apply these features into semi-supervised clustering algorithm for efficient data classification, analysis and feature extraction. After that, flow service requirement could be predicted through extraction of user, flow and data plane load features and implementation of supervised classification algorithm. Furthermore, we propose corresponding handling strategies related to data plane topology, flow forwarding and multi-constraint weight assignment, and proposes personalized routing selection mechanism.

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Authors and Affiliations

  1. College of Mathematics and Computer Science, Fuzhou University, Fuzhou, 350108, China

    Fengqing Chen & Xianghan Zheng

  2. Fujian Key Laboratory of Network Computing and Intelligent Information Processing, Fuzhou, 350108, China

    Fengqing Chen & Xianghan Zheng

Authors
  1. Fengqing Chen
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  2. Xianghan Zheng
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Correspondence to Fengqing Chen .

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Editors and Affiliations

  1. University College of London, London, United Kingdom

    Antonis Bikakis

  2. Fuzhou University, Fuzhou, China

    Xianghan Zheng

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Chen, F., Zheng, X. (2015). Machine-Learning Based Routing Pre-plan for SDN. In: Bikakis, A., Zheng, X. (eds) Multi-disciplinary Trends in Artificial Intelligence. MIWAI 2015. Lecture Notes in Computer Science(), vol 9426. Springer, Cham. https://doi.org/10.1007/978-3-319-26181-2_14

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  • DOI: https://doi.org/10.1007/978-3-319-26181-2_14

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-26180-5

  • Online ISBN: 978-3-319-26181-2

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