Fuzzy logic in estimation of traffic burstiness for admission control in broadband networks

Mehrvar, H.R.; Le‐Ngoc, Tho

doi:10.1109/icc.1997.610055

Cited by 4 publications

(3 citation statements)

References 8 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…They applied fuzzy illation to estimate the chance distribution of CLR, that was then a basis for admission management selections. [14] also proposed a CAC scheme using a type-1 FLS to estimate the level of traffic burstiness; they estimated hurts parameters and used them for CAC in an adaptive environment. [15] proposed use of fuzzy logic prediction on connection Admission Control (CAC) and congestion control in high speed networks.…”

Section: Related Workmentioning

confidence: 99%

Comparative Analysis of Neuro- Fuzzy and Simplex Optimization Model for Congestion Control in Atm Network.

Egoigwe¹,

Okika²,

Araoye³

et al. 2020

CTI

View full text Add to dashboard Cite

Congestion always occurred when the transmission rate increased the data handling capacity of the network. Congestion normally arises when the network resources are not managed efficiently. Therefore if the source delivers at a speed higher then service rate queue, the queue size will be higher. Also if the queue size is finite, then the packet will observed delay. MATLAB Software was used to carry out simulations to develop Congestion control optimization Scheme for ATM Network with the aims to reducing the congestion of Enugu ATM Network. The results of the research reveal the minimization of congestion application model for Enugu ATM using optimization and Neuro-fuzzy. The result shows that congestion control model with Optimization and Neuro-fuzzy were 0.00003153 and 0.00002098 respectively. The ATM Congestion was reduced by 0.0000105, which is 18.2% decrease after Neuro-fuzzy controller was used. The results show the application of Neuro-fuzzy model which can use to control and minimized the ATM Congestion of Enugu ATM Network. The result shows that when Neuro-fuzzy is applied the congestion and the packet queue length in the buffer will be minimized.

show abstract

Section: Related Workmentioning

confidence: 99%

Comparative Analysis of Neuro- Fuzzy and Simplex Optimization Model for Congestion Control in Atm Network.

Egoigwe¹,

Okika²,

Araoye³

et al. 2020

CTI

View full text Add to dashboard Cite

show abstract

“…They applied fuzzy inference to estimate the possibility distribution of CLR, which was then a basis for admission control decisions. Mehrvar and Le-Ngoc [25] also proposed a CAC scheme using a type-1 FLS to estimate the level of traffic burstiness; they estimated hurst parameters and used them for CAC in an adaptive environment.…”

Section: Introductionmentioning

confidence: 99%

Connection admission control in ATM networks using survey-based type-2 fuzzy logic systems

Liang

Karnik

Mendel

2000

IEEE Trans. Syst., Man, Cybern. C

175

View full text Add to dashboard Cite

This paper presents a connection admission control (CAC) method that uses a type-2 fuzzy logic system (FLS). Type-2 FLSs can handle linguistic uncertainties. The linguistic knowledge about CAC is obtained from 30 computer network experts. A methodology for representing the linguistic knowledge using type-2 membership functions and processing surveys using type-2 FLS is proposed. The type-2 FLS provides soft decision boundaries, whereas a type-1 FLS provides a hard decision boundary. The soft decision boundaries can coordinate the cell loss ratio (CLR) and bandwidth utilization, which is impossible for the hard decision boundary.

show abstract

“…Hellendoom [8] projected solution based on an integrated fuzzy system performing the estimation of the effective handwidth that has to he reserved for a single connection, then the correction of the estimated effective bandwidth by measuring the network load and by estimating the suitability of the set of existing connections with regard to statistical multiplexing, and, finally, the comparison of the corrected effective bandwidth with the available link capacity. Mehrvar and Le-Ngoc [9] introduced a method based on the aggregated load and Hurst parameter. Fontaine and Smith [lo] proposed a scheme based on a fuzzy logic and artificial neural network and learns automatically about the networks and predicts online the cell loss ratio that a connection will exhibit if it is accepted into the network.…”

Section: -Related Workmentioning

confidence: 99%

Traffic engineering with MPLS using fuzzy logic for application in IP networks

Resende

Rossi²,

Yamakami³

et al.

The 12th IEEE International Conference on Fuzzy Systems, 2003. FUZZ '03.

View full text Add to dashboard Cite

Fuzzy logic in estimation of traffic burstiness for admission control in broadband networks

Cited by 4 publications

References 8 publications

Comparative Analysis of Neuro- Fuzzy and Simplex Optimization Model for Congestion Control in Atm Network.

Comparative Analysis of Neuro- Fuzzy and Simplex Optimization Model for Congestion Control in Atm Network.

Connection admission control in ATM networks using survey-based type-2 fuzzy logic systems

Traffic engineering with MPLS using fuzzy logic for application in IP networks

Contact Info

Product

Resources

About