University of Oulu

Ouamri, MA, Azni, M, Singh, D, Almughalles, W, Muthanna, MSA. Request delay and survivability optimization for software defined-wide area networking (SD-WAN) using multi-agent deep reinforcement learning. Trans Emerging Tel Tech. 2023; 34(7):e4776. doi: 10.1002/ett.4776

Request delay and survivability optimization for software defined-wide area networking (SD-WAN) using multi-agent deep reinforcement learning

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Author: Ouamri, Mohamed Amine1,2; Azni, Mohamed2; Singh, Daljeet3,4;
Organizations: 1Université de Grenoble Alpes, CNRS, LIG, DRAKKAR Teams, Grenoble, France
2Département ATE, Laboratoire d'Informatique Médicale (LIMED), Université de Bejaia, Bejaia, Algeria
3Faculty of Medicine, Research Unit of Health Sciences and Technology, University of Oulu, Oulu, Finland
4Centre for Space Research, Department of Research and Development, School of Electronics and Electrical Engineering, Lovely Professional University, Phagwara, India
5School of Communications and Information Engineering, Chongqing University of Posts and Telecommunication, Chongqing, China
6Institute of Computer Technologies and Information Security, Southern Federal University, Taganrog, Russia
Format: article
Version: accepted version
Access: embargoed
Persistent link: http://urn.fi/urn:nbn:fi-fe2023080894273
Language: English
Published: John Wiley & Sons, 2023
Publish Date: 2024-04-18
Description:

Abstract

Data exchange between headquarters and local branches represents a major challenge issue for business success. For this issue, traditional solutions applied to wide area networks (WAN) remain unrealistic and require a good knowledge of the systems. Recently, software-defined wide area networking (SD-WAN) plays a pivotal role and constitutes, in general, a reliable solution for wide area networking. Compared to the classical WAN, SD-WAN decouples the control plane from gateway devices. Moreover, SD-WAN are based on centralized WAN management with dynamic reconfiguration, allowing it to control the entire service requirements. Nevertheless, traffic management is the most usual metric in SD-WAN. It gives a clear indication on propagation latency, request delay and survivability; that is, network connectivity. In this article, to guarantee an efficient quality of service and deal with the increased delay problem, we investigate the problem of joint optimization of average request delay and survivability in the proposed SD-WAN model. The optimization approaches are formulated so as to minimize the average request delay and maximize the network connectivity. Subsequently, we adopt a multi-agent deep Q-Network algorithm to solve it, where the reward function is reformulated based on the optimization objective. Simulation results show that our strategy improves the system’s performance significantly in terms of request delay and survivability, compared to traditional baseline algorithms.

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Series: Transactions on emerging telecommunications technologies
ISSN: 2161-5748
ISSN-E: 2161-3915
ISSN-L: 2161-5748
Volume: 34
Issue: 7
Article number: e4776
DOI: 10.1002/ett.4776
OADOI: https://oadoi.org/10.1002/ett.4776
Type of Publication: A1 Journal article – refereed
Field of Science: 213 Electronic, automation and communications engineering, electronics
Subjects:
Copyright information: © 2023 John Wiley & Sons, Ltd. This is the peer reviewed version of the following article: Ouamri, MA, Azni, M, Singh, D, Almughalles, W, Muthanna, MSA. Request delay and survivability optimization for software defined-wide area networking (SD-WAN) using multi-agent deep reinforcement learning. Trans Emerging Tel Tech. 2023; 34(7):e4776, which has been published in final form at 10.1002/ett.4776. This article may be used for non-commercial purposes in accordance with Wiley Terms and Conditions for Use of Self-Archived Versions. This article may not be enhanced, enriched or otherwise transformed into a derivative work, without express permission from Wiley or by statutory rights under applicable legislation. Copyright notices must not be removed, obscured or modified. The article must be linked to Wiley’s version of record on Wiley Online Library and any embedding, framing or otherwise making available the article or pages thereof by third parties from platforms, services and websites other than Wiley Online Library must be prohibited.