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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. |
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[1] Elephant flow detection intelligence for software-defined networks: a survey on current techniques and future direction, Hamdan, Mosab, Elshafie, Hashim, Salih, Sayeed, Abdelsalam, Samah, Husain, Omayma, Gismalla, Mohammed S. M., Ghaleb, Mustafa, Marsono, M. N., Evolutionary Intelligence, ISSN 1864-5909, Issue 4, Volume 17, 2024.
Digital Object Identifier: 10.1007/s12065-023-00902-7 [CrossRef]
[2] Machine Learning-Based Crack Detection Methods in Ancient Buildings, Fang, Tianke, Hui, Zhenxing, P.Rey, William, Yang, Aihua, Liu, Bin, He, Yuanrong, Proceedings of the 2024 Guangdong-Hong Kong-Macao Greater Bay Area International Conference on Digital Economy and Artificial Intelligence, ISBN 9798400717147, 2024.
Digital Object Identifier: 10.1145/3675417.3675564 [CrossRef]
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Faculty of Electrical Engineering and Computer Science
Stefan cel Mare University of Suceava, Romania
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