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An Improved Quality-of-Service Performance Using RED's Active Queue Management Flow Control in Classifying Networks

Traffic classification networks have various applications for data transmissions to ensure quality of service (QoS) for various classes of traffic at the routers. Multi-level random early detection (MRED) scheduling algorithm is used to manage resources at the routers guaranteeing QoS. However, the...

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Published in:IEEE access 2017-01, Vol.5, p.24467-24478
Main Authors: Alkharasani, Ameen M., Othman, Mohamed, Abdullah, Azizol, Kweh Yeah Lun
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description Traffic classification networks have various applications for data transmissions to ensure quality of service (QoS) for various classes of traffic at the routers. Multi-level random early detection (MRED) scheduling algorithm is used to manage resources at the routers guaranteeing QoS. However, the MRED queue mechanism is insensitive to traffic and difficult to set parameters, for the average queue is sensitive to high congestion level of multi-flow which is a major issue affecting the performance of the queue in the networks. This paper propose a new scheduling algorithm that manages congestion level by increasing the stability of parameters, using dynamic weighted traffic with redefining probability drop traffic in the MRED algorithm. The results present the performance algorithm while utilizing the reference algorithms, improving the bandwidth fairness and average throughput and reduce the average delay and packet drop.
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source IEEE Open Access Journals
subjects Active control
active queue management
Algorithm design and analysis
Algorithms
Bandwidth
classes of traffic
Classification
Data transmission
Delays
Dynamic stability
Flow control
Heuristic algorithms
marker algorithm
Networks
Parameter sensitivity
Quality of service
Quality of service architectures
Queuing theory
Routers
Scheduling
scheduling algorithm
Scheduling algorithms
Throughput
Traffic congestion
Traffic control
title An Improved Quality-of-Service Performance Using RED's Active Queue Management Flow Control in Classifying Networks
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