Grey wolf optimizer based fuzzy-PI active queue management design for network congestion avoidance
Congestion is one of the most important issues in communication networks which has attracted much research attention. To ensure a stable TCP network, we can use active queue management (AQM for early congestion detection and router queue length regulation. In this study, it was proposed to use the G...
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| Format: | EJournal Article |
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Institute of Advanced Engineering and Science,
2020-04-01.
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| LEADER | 02538 am a22003013u 4500 | ||
|---|---|---|---|
| 001 | ijeecs20304_13591 | ||
| 042 | |a dc | ||
| 100 | 1 | 0 | |a Sabry, Sana Sabah |e author |
| 100 | 1 | 0 | |a University of Information Technology and Communications |e contributor |
| 700 | 1 | 0 | |a Kaittan, Nada Mahdi |e author |
| 245 | 0 | 0 | |a Grey wolf optimizer based fuzzy-PI active queue management design for network congestion avoidance |
| 260 | |b Institute of Advanced Engineering and Science, |c 2020-04-01. | ||
| 500 | |a https://ijeecs.iaescore.com/index.php/IJEECS/article/view/20304 | ||
| 520 | |a Congestion is one of the most important issues in communication networks which has attracted much research attention. To ensure a stable TCP network, we can use active queue management (AQM for early congestion detection and router queue length regulation. In this study, it was proposed to use the Grey Wolf Optimizer (GWO) algorithm in designing a fuzzy proportional integral (fuzzy-PI) controller as a novel AQM for internet routers congestion control and for achieving a low steady-state error and fast response. The suggested Fuzzy logic-based network traffic control strategy permit us to deploy linguistic knowledge for depicting the dynamics of probability marking functions and ensures a more accurate use of multiple inputs to depict the the network's state. The possibility of incorporating human knowledge into such a control strategy using Fuzzy logic control methodology was demonstrated. The postulated controller was compared to proportion integral (PI) through several MATLAB simulation scenarios. The results indicated the stability of the postulated controller and its ability to attain a faster response in a dynamic network with varying network load and target queue length. | ||
| 540 | |a Copyright (c) 2020 Institute of Advanced Engineering and Science | ||
| 540 | |a http://creativecommons.org/licenses/by-nc/4.0 | ||
| 546 | |a eng | ||
| 690 | |a Engineering | ||
| 690 | |a AQM, Congestion Control, Fuzzy-PI, Grey Wolf Optimization, GWO | ||
| 655 | 7 | |a info:eu-repo/semantics/article |2 local | |
| 655 | 7 | |a info:eu-repo/semantics/publishedVersion |2 local | |
| 655 | 7 | |2 local | |
| 786 | 0 | |n Indonesian Journal of Electrical Engineering and Computer Science; Vol 18, No 1: April 2020; 199-208 | |
| 786 | 0 | |n 2502-4760 | |
| 786 | 0 | |n 2502-4752 | |
| 786 | 0 | |n 10.11591/ijeecs.v18.i1 | |
| 787 | 0 | |n https://ijeecs.iaescore.com/index.php/IJEECS/article/view/20304/13591 | |
| 856 | 4 | 1 | |u https://ijeecs.iaescore.com/index.php/IJEECS/article/view/20304/13591 |z Get fulltext |