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...

Full description

Bibliographic Details
Main Author: Hussein Abdel-jaber
Format: Article
Language:English
Published: Elsevier 2015-10-01
Series:Journal of King Saud University: Computer and Information Sciences
Subjects:
Online Access:http://www.sciencedirect.com/science/article/pii/S1319157815000634
id doaj-26cd7de6e217471986124df235969314
record_format Article
spelling 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
_version_ 1725990201783746560