AI Secured SD-WAN Architecture as a Latency Critical IoT Enabler for 5G and Beyond Communications

Asif, Rameez; Ghanem, Kinan

doi:10.1109/ccnc49032.2021.9369477

Cited by 6 publications

(1 citation statement)

References 23 publications

(22 reference statements)

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“…SD-WAN solves this problem with the integration of the cloud. The SD-WAN deploys most cloud benefits, such as the capability to aid several connection types (such as frame relay, MPLS, and LTE/4G/5G wireless interactions), able to provide an expected user experience by using centrally-configured applications, able to provide a user-friendly interface that is easy to access and handle, depend less relying on technology, and automatic provisioning that is simple and flexible (about 10,000+ sites are possible) [9]. In short, SD-WAN chooses the route with the least resistance for application transport that reduces the cost of the WAN and makes the most of available bandwidth by using the cloud to connect to it.…”

Section: Sd-wan For Bandwidth and Delaymentioning

confidence: 99%

SD-WAN for Bandwidth and Delay Improvements on the Internet

Qin

2022

SHS Web Conf.

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Advancement in applications and operational situations place stringent demands on longdistance data transmission, forcing network operators to rethink how they construct wide area networks [1]. Wide-area network improvement that is indicated by software-defined wide area network (SD-WAN) has been lauded as the most promising next-generation wide-area network design. SD-WAN is able to monitor and manage connection links adaptively based on application requirements [2]. This paper will discuss the newly emerging SD-WAN architecture for the implementation of the Internet. A thorough comparison and challenges that WAN faced before SD-WAN was also discussed to further prove the current features and its benefits. Handling any information accessible over the aspect and exploiting 5G transport for those important applications that demand amazingly low latency and huge bandwidths, SD-WAN will provide a smooth shift for the energy industry to an entire 5G connection [3], also will promote the development of the society.

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Section: Sd-wan For Bandwidth and Delaymentioning

confidence: 99%

SD-WAN for Bandwidth and Delay Improvements on the Internet

Qin

2022

SHS Web Conf.

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Techno-Economic Assessment in Communications: New Challenges

Bendicho,

Bendicho

2023

Lecture Notes in Networks and Systems

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A predictive SD‐WAN traffic management method for IoT networks in multi‐datacenters using deep RNN

Nazemi Absardi,

Javidan

2024

IET Communications

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Deploying the Internet of Things (IoT) in integrated edge‐cloud environments exposes the IoT traffic data to performance issues such as delay, bandwidth limitation etc. Recently, Software‐Defined Wide Area Network (SD‐WAN) has emerged as an architecture that originates from the Software‐Defined Network (SDN) paradigm and provides solutions for networking multiple data centers by allowing network administrators to manage and control network layers. In this article, an SDWAN‐based policy for traffic management in IoT is introduced in which the Quality of Service (QoS) metrics such as end‐to‐end delay and bandwidth utilization are optimized. The proposed method implements the traffic management policy in the SDWAN controller. When the IoT traffic flows reach the SDWAN infrastructure network, graph search algorithms are performed to find the near‐optimal paths that affect the end‐to‐end delay of traffic flows. Because of the ability of deep learning to process complex data, a deep RNN model is used to predict the network state information, such as link latency and available bandwidth, before the traffic flows reach the infrastructure network. The proposed method consists of four key modules to predict the routes for future time intervals: (a) an SD‐WAN topology updater unit that checks the link changes and availability, (b) the network state information collector, which collects the network state information to create a dataset, (c) the learning unit, which trains a deep RNN model using the created dataset, and (d) the route predictor unit, which uses the trained model to predict the network state information using a heuristic algorithm to determine the routes. The simulation results showed that the deep RNN model can achieve high accuracy and low Mean Absolute Error (MAE), and the proposed method outperforms shortest‐path algorithms in terms of latency. At the same time, the available bandwidth is almost fairly distributed among all network links.

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AI Secured SD-WAN Architecture as a Latency Critical IoT Enabler for 5G and Beyond Communications

Cited by 6 publications

References 23 publications

SD-WAN for Bandwidth and Delay Improvements on the Internet

SD-WAN for Bandwidth and Delay Improvements on the Internet

Techno-Economic Assessment in Communications: New Challenges

A predictive SD‐WAN traffic management method for IoT networks in multi‐datacenters using deep RNN

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