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Journal of Network and Systems Management

Erschienen in:

01.04.2021

Machine Learning-Based Multipath Routing for Software Defined Networks

verfasst von: Mohamad Khattar Awad, Marwa Hassan Hafez Ahmed, Ali F. Almutairi, Imtiaz Ahmad

Erschienen in: Journal of Network and Systems Management | Ausgabe 2/2021

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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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Titel: Machine Learning-Based Multipath Routing for Software Defined Networks
verfasst von: Mohamad Khattar Awad
Marwa Hassan Hafez Ahmed
Ali F. Almutairi
Imtiaz Ahmad
Publikationsdatum: 01.04.2021
Verlag: Springer US
Erschienen in: Journal of Network and Systems Management / Ausgabe 2/2021
Print ISSN: 1064-7570
Elektronische ISSN: 1573-7705
DOI: https://doi.org/10.1007/s10922-020-09583-4

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