Parameter Adjustment Using Neural-Network-Based Genetic Algorithms for Guaranteed QOS in ATM Networks
Summary :
A number of flexible control mechanisms used in buffer management, congestion control and bandwidth allocation have been proposed to improve the performance of ATM networks by introducing parameters, such as threshold, push-out probability and incremental bandwidth size of a virtual path, which are adjustable by network providers. However, it is difficult to adaptively adjust these parameters, since the traffic in ATM networks is further complicated by accommodating various kinds of services. To overcome the problem, we propose in this paper a control scheme based on the genetic algorithms and the neural estimator. The neural estimator forecasts the future QOS values for each candidate parameter set, and the genetic algorithms select the best one to control the real network. An example of buffer management in an ATM switch is examined in this paper. Simulation results show the effectiveness of the proposed control scheme in adaptively adjusting the parameter set even when the traffic environment and the QOS requirements are dynamically changing.
- Publication
- IEICE TRANSACTIONS on Communications Vol.E78-B No.4 pp.572-579
- Publication Date
- 1995/04/25
- Publicized
- Online ISSN
- DOI
- Type of Manuscript
- Special Section PAPER (Special Issue on Traffic and Quality Control for Communication Networks)
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Li-Der CHOU, Jean-Lien C. WU, "Parameter Adjustment Using Neural-Network-Based Genetic Algorithms for Guaranteed QOS in ATM Networks" in IEICE TRANSACTIONS on Communications,
vol. E78-B, no. 4, pp. 572-579, April 1995, doi: .
Abstract: A number of flexible control mechanisms used in buffer management, congestion control and bandwidth allocation have been proposed to improve the performance of ATM networks by introducing parameters, such as threshold, push-out probability and incremental bandwidth size of a virtual path, which are adjustable by network providers. However, it is difficult to adaptively adjust these parameters, since the traffic in ATM networks is further complicated by accommodating various kinds of services. To overcome the problem, we propose in this paper a control scheme based on the genetic algorithms and the neural estimator. The neural estimator forecasts the future QOS values for each candidate parameter set, and the genetic algorithms select the best one to control the real network. An example of buffer management in an ATM switch is examined in this paper. Simulation results show the effectiveness of the proposed control scheme in adaptively adjusting the parameter set even when the traffic environment and the QOS requirements are dynamically changing.
URL: https://global.ieice.org/en_transactions/communications/10.1587/e78-b_4_572/_p
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@ARTICLE{e78-b_4_572,
author={Li-Der CHOU, Jean-Lien C. WU, },
journal={IEICE TRANSACTIONS on Communications},
title={Parameter Adjustment Using Neural-Network-Based Genetic Algorithms for Guaranteed QOS in ATM Networks},
year={1995},
volume={E78-B},
number={4},
pages={572-579},
abstract={A number of flexible control mechanisms used in buffer management, congestion control and bandwidth allocation have been proposed to improve the performance of ATM networks by introducing parameters, such as threshold, push-out probability and incremental bandwidth size of a virtual path, which are adjustable by network providers. However, it is difficult to adaptively adjust these parameters, since the traffic in ATM networks is further complicated by accommodating various kinds of services. To overcome the problem, we propose in this paper a control scheme based on the genetic algorithms and the neural estimator. The neural estimator forecasts the future QOS values for each candidate parameter set, and the genetic algorithms select the best one to control the real network. An example of buffer management in an ATM switch is examined in this paper. Simulation results show the effectiveness of the proposed control scheme in adaptively adjusting the parameter set even when the traffic environment and the QOS requirements are dynamically changing.},
keywords={},
doi={},
ISSN={},
month={April},}
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TY - JOUR
TI - Parameter Adjustment Using Neural-Network-Based Genetic Algorithms for Guaranteed QOS in ATM Networks
T2 - IEICE TRANSACTIONS on Communications
SP - 572
EP - 579
AU - Li-Der CHOU
AU - Jean-Lien C. WU
PY - 1995
DO -
JO - IEICE TRANSACTIONS on Communications
SN -
VL - E78-B
IS - 4
JA - IEICE TRANSACTIONS on Communications
Y1 - April 1995
AB - A number of flexible control mechanisms used in buffer management, congestion control and bandwidth allocation have been proposed to improve the performance of ATM networks by introducing parameters, such as threshold, push-out probability and incremental bandwidth size of a virtual path, which are adjustable by network providers. However, it is difficult to adaptively adjust these parameters, since the traffic in ATM networks is further complicated by accommodating various kinds of services. To overcome the problem, we propose in this paper a control scheme based on the genetic algorithms and the neural estimator. The neural estimator forecasts the future QOS values for each candidate parameter set, and the genetic algorithms select the best one to control the real network. An example of buffer management in an ATM switch is examined in this paper. Simulation results show the effectiveness of the proposed control scheme in adaptively adjusting the parameter set even when the traffic environment and the QOS requirements are dynamically changing.
ER -
English
