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

Author:

Sun Wenwen12,Guan Shaopeng1

Affiliation:

1. School of Information and Electronic Engineering, Shandong Technology and Business University, Yantai, Shandong, China

2. School of Electronic and Information Engineering, Huaibei Institute of Technology, Huaibei, Anhui, China

Abstract

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.

Funder

Scientific Research Projects of Universities in Anhui Province

Publisher

PeerJ

Subject

General Computer Science

Reference40 articles.

1. CyberPetri at CDX 2016: real-time network situation awareness;Arendt,2016

2. SDN-based real-time urban traffic analysis in VANET environment;Bhatia;Computer Communications,2020

3. Detection of zero-day attacks: an unsupervised port-based approach;Blaise;Computer Networks,2020

4. Accelerating evolutionary algorithms with Gaussian process fitness function models;Buche;IEEE Transactions on Systems, Man and Cybernetics, Part C (Applications and Reviews),2005

5. Encoding high-cardinality string categorical variables;Cerda;IEEE Transactions on Knowledge and Data Engineering,2020

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