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Machine Learning-Based Multipath Routing for Software Defined Networks

Authors
  • Awad, Mohamad Khattar1
  • Ahmed, Marwa Hassan Hafez1
  • Almutairi, Ali F.1
  • Ahmad, Imtiaz1
  • 1 Kuwait University, Kuwait, Kuwait , Kuwait (Kuwait)
Type
Published Article
Journal
Journal of Network and Systems Management
Publisher
Springer US
Publication Date
Jan 20, 2021
Volume
29
Issue
2
Identifiers
DOI: 10.1007/s10922-020-09583-4
Source
Springer Nature
Keywords
License
Yellow

Abstract

Network softwarization has recently been enabled via the software-defined networking (SDN) paradigm, which separates the data plane from control plane allowing for a flexible and centralized control of networks. This separation facilitates implementation of machine learning techniques for network management and optimization. In this work, a machine learning-based multipath routing (MLMR) framework is proposed for software-defined networks with quality-of-service (QoS) constraints and flow rules space constraints. The QoS-aware multipath routing problem in SDN is modeled as multicommodity network flow problem with side constraints, that is known to be NP-hard. The proposed framework utilizes network status estimates, and their corresponding routing configurations available at the network central controller to learn a mapping function between them. Once the mapping function is learned, it is applied on live-inputs of network status and routing requests to predict a multipath routing solutions in real-time. Performance evaluations of the MLMR framework on real traces of network traffic verify its accuracy and resilience to noise in training data. Furthermore, the MLMR framework demonstrates more than 98.99% improvement in computational efficiency.

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