Performance study of Active Queue Management methods: Adaptive GRED, REDD, and GRED-Linear analytical model
Congestion control is one of the hot research topics that helps maintain the performance of computer networks. This paper compares three Active Queue Management (AQM) methods, namely, Adaptive Gentle Random Early Detection (Adaptive GRED), Random Early Dynamic Detection (REDD), and GRED Linear analy...
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doaj-26cd7de6e217471986124df2359693142020-11-24T21:23:59ZengElsevierJournal of King Saud University: Computer and Information Sciences1319-15782015-10-0127441642910.1016/j.jksuci.2015.01.003Performance study of Active Queue Management methods: Adaptive GRED, REDD, and GRED-Linear analytical modelHussein Abdel-jaberCongestion control is one of the hot research topics that helps maintain the performance of computer networks. This paper compares three Active Queue Management (AQM) methods, namely, Adaptive Gentle Random Early Detection (Adaptive GRED), Random Early Dynamic Detection (REDD), and GRED Linear analytical model with respect to different performance measures. Adaptive GRED and REDD are implemented based on simulation, whereas GRED Linear is implemented as a discrete-time analytical model. Several performance measures are used to evaluate the effectiveness of the compared methods mainly mean queue length, throughput, average queueing delay, overflow packet loss probability, and packet dropping probability. The ultimate aim is to identify the method that offers the highest satisfactory performance in non-congestion or congestion scenarios. The first comparison results that are based on different packet arrival probability values show that GRED Linear provides better mean queue length; average queueing delay and packet overflow probability than Adaptive GRED and REDD methods in the presence of congestion. Further and using the same evaluation measures, Adaptive GRED offers a more satisfactory performance than REDD when heavy congestion is present. When the finite capacity of queue values varies the GRED Linear model provides the highest satisfactory performance with reference to mean queue length and average queueing delay and all the compared methods provide similar throughput performance. However, when the finite capacity value is large, the compared methods have similar results in regard to probabilities of both packet overflowing and packet dropping.http://www.sciencedirect.com/science/article/pii/S1319157815000634Adaptive GREDDiscrete-time queuesGRED Linear analytical modelPerformance measuresREDD |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Hussein Abdel-jaber |
spellingShingle |
Hussein Abdel-jaber Performance study of Active Queue Management methods: Adaptive GRED, REDD, and GRED-Linear analytical model Journal of King Saud University: Computer and Information Sciences Adaptive GRED Discrete-time queues GRED Linear analytical model Performance measures REDD |
author_facet |
Hussein Abdel-jaber |
author_sort |
Hussein Abdel-jaber |
title |
Performance study of Active Queue Management methods: Adaptive GRED, REDD, and GRED-Linear analytical model |
title_short |
Performance study of Active Queue Management methods: Adaptive GRED, REDD, and GRED-Linear analytical model |
title_full |
Performance study of Active Queue Management methods: Adaptive GRED, REDD, and GRED-Linear analytical model |
title_fullStr |
Performance study of Active Queue Management methods: Adaptive GRED, REDD, and GRED-Linear analytical model |
title_full_unstemmed |
Performance study of Active Queue Management methods: Adaptive GRED, REDD, and GRED-Linear analytical model |
title_sort |
performance study of active queue management methods: adaptive gred, redd, and gred-linear analytical model |
publisher |
Elsevier |
series |
Journal of King Saud University: Computer and Information Sciences |
issn |
1319-1578 |
publishDate |
2015-10-01 |
description |
Congestion control is one of the hot research topics that helps maintain the performance of computer networks. This paper compares three Active Queue Management (AQM) methods, namely, Adaptive Gentle Random Early Detection (Adaptive GRED), Random Early Dynamic Detection (REDD), and GRED Linear analytical model with respect to different performance measures. Adaptive GRED and REDD are implemented based on simulation, whereas GRED Linear is implemented as a discrete-time analytical model. Several performance measures are used to evaluate the effectiveness of the compared methods mainly mean queue length, throughput, average queueing delay, overflow packet loss probability, and packet dropping probability. The ultimate aim is to identify the method that offers the highest satisfactory performance in non-congestion or congestion scenarios. The first comparison results that are based on different packet arrival probability values show that GRED Linear provides better mean queue length; average queueing delay and packet overflow probability than Adaptive GRED and REDD methods in the presence of congestion. Further and using the same evaluation measures, Adaptive GRED offers a more satisfactory performance than REDD when heavy congestion is present. When the finite capacity of queue values varies the GRED Linear model provides the highest satisfactory performance with reference to mean queue length and average queueing delay and all the compared methods provide similar throughput performance. However, when the finite capacity value is large, the compared methods have similar results in regard to probabilities of both packet overflowing and packet dropping. |
topic |
Adaptive GRED Discrete-time queues GRED Linear analytical model Performance measures REDD |
url |
http://www.sciencedirect.com/science/article/pii/S1319157815000634 |
work_keys_str_mv |
AT husseinabdeljaber performancestudyofactivequeuemanagementmethodsadaptivegredreddandgredlinearanalyticalmodel |
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