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Stefan cel Mare
University of Suceava
Faculty of Electrical Engineering and
Computer Science
13, Universitatii Street
Suceava - 720229
ROMANIA

Print ISSN: 1582-7445
Online ISSN: 1844-7600
WorldCat: 643243560
doi: 10.4316/AECE


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Analysis of the Hybrid PSO-InC MPPT for Different Partial Shading Conditions, LEOPOLDINO, A. L. M., FREITAS, C. M., MONTEIRO, L. F. C.
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  3/2023 - 7

Efficient Routing by Detecting Elephant Flows with Deep Learning Method in SDN

AYMAZ, S. See more information about AYMAZ, S. on SCOPUS See more information about AYMAZ, S. on IEEExplore See more information about AYMAZ, S. on Web of Science, CAVDAR, T. See more information about CAVDAR, T. on SCOPUS See more information about CAVDAR, T. on SCOPUS See more information about CAVDAR, T. on Web of Science
 
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Download PDF pdficon (1,342 KB) | Citation | Downloads: 485 | Views: 846

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
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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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References Weight

Web of Science® Citations for all references: 330 TCR
SCOPUS® Citations for all references: 3,374 TCR

Web of Science® Average Citations per reference: 11 ACR
SCOPUS® Average Citations per reference: 109 ACR

TCR = Total Citations for References / ACR = Average Citations per Reference

We introduced in 2010 - for the first time in scientific publishing, the term "References Weight", as a quantitative indication of the quality ... Read more

Citations for references updated on 2024-07-17 17:04 in 205 seconds.




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