A neural-fuzzy system for congestion control in ATM networks

We propose the use of a neural-fuzzy scheme for rate-based feedback congestion control in asynchronous transfer mode (ATM) networks. Available bit rate (ABR) traffic is not guaranteed quality of service (QoS) in the setup connection, and it can dynamically share the available bandwidth. Therefore, c...

Full description

Saved in:
Bibliographic Details
Published in:IEEE transactions on cybernetics 2000-02, Vol.30 (1), p.2-9
Main Authors: Lee, S J, Hou, C L
Format: Article
Language:English
Subjects:
Asynchronous transfer mode
Bandwidth
Bit rate
Communication system control
Communication system traffic control
Congestion
Control systems
Cybernetics
Fixed rates
Intelligent networks
Networks
Quality of service
Queues
Studies
Switches
Traffic control
Traffic engineering
Traffic flow
Citations: Items that this one cites
Items that cite this one
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:We propose the use of a neural-fuzzy scheme for rate-based feedback congestion control in asynchronous transfer mode (ATM) networks. Available bit rate (ABR) traffic is not guaranteed quality of service (QoS) in the setup connection, and it can dynamically share the available bandwidth. Therefore, congestion can be controlled by regulating the source rate, to a certain degree, according to the current traffic flow. Traditional methods perform congestion control by monitoring the queue length. The source rate is decreased by a fixed rate when the queue length is greater than a prespecified threshold. However, it is difficult to get a suitable rate according to the degree of traffic congestion. We employ a neural-fuzzy mechanism to control the source rate. Through learning, membership values can be generated and cell loss can be predicted from the status of the queue length. Then, an explicit rate is calculated and the source rate is controlled appropriately. Simulation results have shown that our method is effective compared with traditional methods.
ISSN:1083-4419
2168-2267
1941-0492
2168-2275
DOI:10.1109/3477.826942