Uncertainty Measures associated with Fuzzy Rules for Connection Admission Control in ATM Networks

1998 ◽  
pp. 177-197
Author(s):  
Maria Fernanda N. Ramalho
Keyword(s):  
Admission Control ◽  
Fuzzy Rules ◽  
Connection Admission Control ◽  
Atm Networks ◽  
Uncertainty Measures ◽  
Connection Admission

scholarly journals Fuzzy Inference Based Connection Admission Control in ATM Networks

1997 ◽  
Vol 1 (1) ◽  
pp. 14-22
Author(s):  
Kiyohiko Uehara ◽  
◽  
Kaoru Hirota ◽  
Keyword(s):  
Admission Control ◽  
Upper Bound ◽  
Fuzzy Inference ◽  
Learning Algorithm ◽  
Fuzzy Rules ◽  
Connection Admission Control ◽  
Atm Networks ◽  
Back Propagation Algorithm ◽  
Inference Method ◽  
Connection Admission

A connection admission control (CAC) method is proposed for asynchronous transfer mode (ATM) networks by applying the fuzzy inference and learning algorithm of neural networks. In order to guarantee the allowed cell loss ratio (CLR) in CAC, the upper bound of CLR must be used as the criterion for judging whether an incoming call can be accepted or not. For estimating the upper bound of CLR from observed CLR data, fuzzy inference, based on a weighted mean of fuzzy sets, is adopted. This inference method can effectively estimate the possibility distribution of CLR by applying the error back-propagation algorithm with the proposed energy functions in learning and provide the upper bound of CLR efficiently from the distribution. A self-compensation mechanism for estimation errors is also provided, which is simple enough to work in real time by taking advantage of the fuzzy inference method adopted. Fuzzy rules in the area with no observed data are generated by extrapolation from adjacent fuzzy rules in the area with observed data. This increases the multiplex gain, thereby guaranteeing the allowed CLR as much as possible. The simulation results show the feasibility of the proposed CAC method.

scholarly journals Sustaining Negotiated QoS in Connection Admission Control for ATM Networks Using Fuzzy Logic Techniques

10.28945/3275 ◽  
2008 ◽  
Author(s):  
Olufade F. Williams Onifade ◽  
G. Adesola Aderounmu ◽  
Oluwasikemi Tayo
Keyword(s):  
Fuzzy Logic ◽  
Admission Control ◽  
Bandwidth Allocation ◽  
Connection Admission Control ◽  
Atm Networks ◽  
Output Stage ◽  
Centroid Method ◽  
Average Cell ◽  
Connection Admission ◽  
Fuzzy Logic Technique

The objective of connection admission control (CAC) is to keep the network load moderate to achieve a performance objective associated with Quality of Service (QoS). Cell Loss Ratio (CLR), a key QoS parameter in ATM networks, is essential for proper network resources dimensioning, congestion control, bandwidth allocation and routing. In this research, we employed fuzzy logic technique in Statistical Connection Admission Control (S-CAC) - a CAC employing multiplexing of the bandwidth between the peak cell rate and the sustained (average) cell rate. The fuzzy technique consists of an input stage, a processing stage, and an output stage. We defined the rules with “max-min” inference method in which the output membership function is given the truth value generated by the premise. The results was defuzzi-fied to a crisp value using the “centroid” method which favors the rule with the output of the greatest area, and the result thereafter charted to compare it operation.

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