| 3/2023 - 7 |
Efficient Routing by Detecting Elephant Flows with Deep Learning Method in SDNAYMAZ, S.   , CAVDAR, T. |
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Author keywords
load flow control, machine learning algorithms, particle swarm optimization, routing, software defined networking
References keywords
flow(14), networks(13), data(12), load(9), software(8), detection(8), routing(7), defined(7), balancing(7), elephant(6)
Blue keywords are present in both the references section and the paper title.
About this article
Date of Publication: 2023-08-31
Volume 23, Issue 3, Year 2023, On page(s): 57 - 66
ISSN: 1582-7445, e-ISSN: 1844-7600
Digital Object Identifier: 10.4316/AECE.2023.03007
Web of Science Accession Number: 001062641900007
SCOPUS ID: 85172349574
Abstract
Nowadays, the increase in the number of devices using local and global networks has made it very challenging to manage network traffic, especially during epidemics and natural disasters. Traffic must be analyzed and routed efficiently while managing the network in these situations. The proposed approach focuses on effective routing by detecting elephant flows. In this study, the Deep Learning method has been utilized for elephant flow detection. In flow detection, 11 different features are extracted for each flow, and elephant flows are accurately detected. Additionally, the Grid Search method determines the parameters that yield the best results in classifying elephant and mice flows. As a result, elephant flows that have been classified are routed using the Discrete-Particle Optimization method, whereas mice flows are routed using traditional Round Robin or Random methods. The experimental results show that the proposed approach achieves a high level of accuracy in detecting elephant flows and routing them effectively while also maintaining the overall network performance. |
| References | | | Cited By «-- Click to see who has cited this paper |
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Faculty of Electrical Engineering and Computer Science
Stefan cel Mare University of Suceava, Romania
All rights reserved: Advances in Electrical and Computer Engineering is a registered trademark of the Stefan cel Mare University of Suceava. No part of this publication may be reproduced, stored in a retrieval system, photocopied, recorded or archived, without the written permission from the Editor. When authors submit their papers for publication, they agree that the copyright for their article be transferred to the Faculty of Electrical Engineering and Computer Science, Stefan cel Mare University of Suceava, Romania, if and only if the articles are accepted for publication. The copyright covers the exclusive rights to reproduce and distribute the article, including reprints and translations.
Permission for other use: The copyright owner's consent does not extend to copying for general distribution, for promotion, for creating new works, or for resale. Specific written permission must be obtained from the Editor for such copying. Direct linking to files hosted on this website is strictly prohibited.
Disclaimer: Whilst every effort is made by the publishers and editorial board to see that no inaccurate or misleading data, opinions or statements appear in this journal, they wish to make it clear that all information and opinions formulated in the articles, as well as linguistic accuracy, are the sole responsibility of the author.
