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

Wenwen Sun; Shaopeng Guan

doi:10.7717/peerj-cs.1011

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

PeerJ Comput Sci. 2022 Jun 23:8:e1011. doi: 10.7717/peerj-cs.1011. eCollection 2022.

Authors

Wenwen Sun^{1

2}, Shaopeng Guan¹

Affiliations

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.

Keywords: GRU; Multiple domains; Salp swarm algorithm; Software-defined networking; Traffic situation prediction.

Associated data

figshare/10.6084/m9.figshare.18467507.v1

Grants and funding

This work was supported by the Scientific Research Projects of Universities in Anhui Province (KJ2021A1243). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.