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A GRU-based traffic situation prediction method in multi-domain software defined network

With the continuous development and improvement of Software-Defined Networking (SDN), large-scale networks are divided into multiple domains. Each domain, which is managed by a controller, forms multi-domain SDN architecture. In multi-domain SDN, the dynamics and complexity are more significant, bri...

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Detalles Bibliográficos
Autores principales: Sun, Wenwen, Guan, Shaopeng
Formato: Online Artículo Texto
Lenguaje:English
Publicado: PeerJ Inc. 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9299279/
https://www.ncbi.nlm.nih.gov/pubmed/35875644
http://dx.doi.org/10.7717/peerj-cs.1011
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author Sun, Wenwen
Guan, Shaopeng
author_facet Sun, Wenwen
Guan, Shaopeng
author_sort Sun, Wenwen
collection PubMed
description With the continuous development and improvement of Software-Defined Networking (SDN), large-scale networks are divided into multiple domains. Each domain, which is managed by a controller, forms multi-domain SDN architecture. In multi-domain SDN, the dynamics and complexity are more significant, bringing great challenges to network management. Comprehensively and accurately predicting traffic situation in multi-domain SDN can better maintain network stability. In this article, we propose a traffic situation prediction method based on the gated recurrent unit (GRU) network in multi-domain SDN. We first analyzed the relevant factors that affect data traffic and control traffic and transformed them into a time series of actual situation values. Then, to enhance the prediction performance of GRU, we used the salp swarm algorithm to optimize the hyperparameters of GRU automatically. Finally, we adopted hyperparameter optimized GRU to achieve traffic situation prediction in multi-domain SDN. The experimental results indicate that the proposed method outperforms other traditional machine learning algorithms in terms of prediction accuracy.
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spelling pubmed-92992792022-07-21 A GRU-based traffic situation prediction method in multi-domain software defined network Sun, Wenwen Guan, Shaopeng PeerJ Comput Sci Artificial Intelligence With the continuous development and improvement of Software-Defined Networking (SDN), large-scale networks are divided into multiple domains. Each domain, which is managed by a controller, forms multi-domain SDN architecture. In multi-domain SDN, the dynamics and complexity are more significant, bringing great challenges to network management. Comprehensively and accurately predicting traffic situation in multi-domain SDN can better maintain network stability. In this article, we propose a traffic situation prediction method based on the gated recurrent unit (GRU) network in multi-domain SDN. We first analyzed the relevant factors that affect data traffic and control traffic and transformed them into a time series of actual situation values. Then, to enhance the prediction performance of GRU, we used the salp swarm algorithm to optimize the hyperparameters of GRU automatically. Finally, we adopted hyperparameter optimized GRU to achieve traffic situation prediction in multi-domain SDN. The experimental results indicate that the proposed method outperforms other traditional machine learning algorithms in terms of prediction accuracy. PeerJ Inc. 2022-06-23 /pmc/articles/PMC9299279/ /pubmed/35875644 http://dx.doi.org/10.7717/peerj-cs.1011 Text en © 2022 Sun and Guan https://creativecommons.org/licenses/by/4.0/This is an open access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, reproduction and adaptation in any medium and for any purpose provided that it is properly attributed. For attribution, the original author(s), title, publication source (PeerJ Computer Science) and either DOI or URL of the article must be cited.
spellingShingle Artificial Intelligence
Sun, Wenwen
Guan, Shaopeng
A GRU-based traffic situation prediction method in multi-domain software defined network
title A GRU-based traffic situation prediction method in multi-domain software defined network
title_full A GRU-based traffic situation prediction method in multi-domain software defined network
title_fullStr A GRU-based traffic situation prediction method in multi-domain software defined network
title_full_unstemmed A GRU-based traffic situation prediction method in multi-domain software defined network
title_short A GRU-based traffic situation prediction method in multi-domain software defined network
title_sort gru-based traffic situation prediction method in multi-domain software defined network
topic Artificial Intelligence
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9299279/
https://www.ncbi.nlm.nih.gov/pubmed/35875644
http://dx.doi.org/10.7717/peerj-cs.1011
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