ATM Connection Admission Control Using Traffic Parameters Compression


KIPS Transactions on Computer and Communication Systems, Vol. 8, No. 3, pp. 311-318, Jun. 2001
10.3745/KIPSTC.2001.8.3.311,   PDF Download:

Abstract

This paper proposes a connection admission control method based on the compression of traffic parameters. We evaluate and compare the performance of the proposed method according to typical compression methods, K-means, CL (Competitive Learning), Fuzzy ISODATA and FNC (Fuzzy Neural Clustering) algorithm. These algorithms are used to compress a number of traffic parameters (variance and mean of observed cell stream). The proposed CAC first estimates the characteristics of input traffic pattern using Fuzzy Mapping Function, and then it decides whether input traffic should be accepted or rejected, using a feedforward Neural Network. We simulate to compare the performance of connection admission control according to used compression methods. The simulation results show that the CAC using FNC algorithm outperforms other methods. To more improve CAC performance, we also found that, if variance of a frame is high, fuzziness value(F) should be small ; otherwise, if variance of a frame is low, fuzziness value should be large.


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Cite this article
[IEEE Style]
J. Y. Lee, "ATM Connection Admission Control Using Traffic Parameters Compression," KIPS Journal C (2001 ~ 2012) , vol. 8, no. 3, pp. 311-318, 2001. DOI: 10.3745/KIPSTC.2001.8.3.311.

[ACM Style]
Jin Yi Lee. 2001. ATM Connection Admission Control Using Traffic Parameters Compression. KIPS Journal C (2001 ~ 2012) , 8, 3, (2001), 311-318. DOI: 10.3745/KIPSTC.2001.8.3.311.