Weight Queue Dynamic Active Queue Management Algorithm

The current problem of packets generation and transformation around the world is router congestion, which then leads to a decline in the network performance in term of queuing delay (D) and packet loss (P<sub>L</sub>). The existing active queue management (AQM) algorithms do not optimize...

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Main Author: Mahmoud Baklizi
Format: Article
Language:English
Published: MDPI AG 2020-12-01
Series:Symmetry
Subjects:
Online Access:https://www.mdpi.com/2073-8994/12/12/2077
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spelling doaj-a6de18e784ae45aebb73b9a7a7a262a42020-12-15T00:04:52ZengMDPI AGSymmetry2073-89942020-12-01122077207710.3390/sym12122077Weight Queue Dynamic Active Queue Management AlgorithmMahmoud Baklizi0Department of Computer Networks Systems, The world Islamic science & education university W.I.S.E, Amman 00962, JordanThe current problem of packets generation and transformation around the world is router congestion, which then leads to a decline in the network performance in term of queuing delay (D) and packet loss (P<sub>L</sub>). The existing active queue management (AQM) algorithms do not optimize the network performance because these algorithms use static techniques for detecting and reacting to congestion at the router buffer. In this paper, a weight queue active queue management (WQDAQM) based on dynamic monitoring and reacting is proposed. Queue weight and the thresholds are dynamically adjusted based on the traffic load. WQDAQM controls the queue within the router buffer by stabilizing the queue weight between two thresholds dynamically. The WQDAQM algorithm is simulated and compared with the existing active queue management algorithms. The results reveal that the proposed method demonstrates better performance in terms mean queue length, D, P<sub>L</sub>, and dropping probability, compared to gentle random early detection (GRED), dynamic GRED, and stabilized dynamic GRED in both heavy or no-congestion cases. In detail, in a heavy congestion status, the proposed algorithm overperformed dynamic GRED (DGRED) by 13.3%, GRED by 19.2%, stabilized dynamic GRED (SDGRED) by 6.7% in term of mean queue length (mql). In terms of D in a heavy congestion status, the proposed algorithm overperformed DGRED by 13.3%, GRED by 19.3%, SDGRED by 6.3%. As for PL, the proposed algorithm overperformed DGRED by 15.5%, SDGRED by 19.8%, GRED by 86.3% in term of PL.https://www.mdpi.com/2073-8994/12/12/2077congestion algorithmsGREDSDGREDimplementationqueue weight
collection DOAJ
language English
format Article
sources DOAJ
author Mahmoud Baklizi
spellingShingle Mahmoud Baklizi
Weight Queue Dynamic Active Queue Management Algorithm
Symmetry
congestion algorithms
GRED
SDGRED
implementation
queue weight
author_facet Mahmoud Baklizi
author_sort Mahmoud Baklizi
title Weight Queue Dynamic Active Queue Management Algorithm
title_short Weight Queue Dynamic Active Queue Management Algorithm
title_full Weight Queue Dynamic Active Queue Management Algorithm
title_fullStr Weight Queue Dynamic Active Queue Management Algorithm
title_full_unstemmed Weight Queue Dynamic Active Queue Management Algorithm
title_sort weight queue dynamic active queue management algorithm
publisher MDPI AG
series Symmetry
issn 2073-8994
publishDate 2020-12-01
description The current problem of packets generation and transformation around the world is router congestion, which then leads to a decline in the network performance in term of queuing delay (D) and packet loss (P<sub>L</sub>). The existing active queue management (AQM) algorithms do not optimize the network performance because these algorithms use static techniques for detecting and reacting to congestion at the router buffer. In this paper, a weight queue active queue management (WQDAQM) based on dynamic monitoring and reacting is proposed. Queue weight and the thresholds are dynamically adjusted based on the traffic load. WQDAQM controls the queue within the router buffer by stabilizing the queue weight between two thresholds dynamically. The WQDAQM algorithm is simulated and compared with the existing active queue management algorithms. The results reveal that the proposed method demonstrates better performance in terms mean queue length, D, P<sub>L</sub>, and dropping probability, compared to gentle random early detection (GRED), dynamic GRED, and stabilized dynamic GRED in both heavy or no-congestion cases. In detail, in a heavy congestion status, the proposed algorithm overperformed dynamic GRED (DGRED) by 13.3%, GRED by 19.2%, stabilized dynamic GRED (SDGRED) by 6.7% in term of mean queue length (mql). In terms of D in a heavy congestion status, the proposed algorithm overperformed DGRED by 13.3%, GRED by 19.3%, SDGRED by 6.3%. As for PL, the proposed algorithm overperformed DGRED by 15.5%, SDGRED by 19.8%, GRED by 86.3% in term of PL.
topic congestion algorithms
GRED
SDGRED
implementation
queue weight
url https://www.mdpi.com/2073-8994/12/12/2077
work_keys_str_mv AT mahmoudbaklizi weightqueuedynamicactivequeuemanagementalgorithm
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