Abstract
Traffic prediction is significant to QoS design Decause it assists efficient management ot network resources to improve the reliability and performance of the next generation Internet. The unavoidable traffic variation caused by diverse Internet services complicates traffic prediction, particularly in a multi-hop network. To simplify the complicated statistical analysis used in traditional approaches, an adaptive trattic prediction approach featuring robustness, high accuracy and high adaptability is proposed in this paper. The proposed approach bases on a novel fuzzy clustering algorithm to generalize and unveil the hidden structure ot traffic patterns. The unveiled structure represents the characteristics of the target traffic. Therefore, it can be referenced to predict traffic in a limited time period by fuzzy matching. To track the variation ot target traffic, the proposed approach adopts an incremental ana dynamic on-line clustering procedure so that the prediction can maintain high accuracy under traffic variation, to verify the performance ot the proposed approach and investigate its properties, the periodical, roisson and real video traffic patterns have been used to experiment, The experimental results showed an excellent performance of the developed adaptive predictor, The prediction errors, in average, are near 2.2 %, 13.6% and 7.62% for periodical, Poisson and real video traffics, respectively.
Original language | English |
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Pages (from-to) | 180-188 |
Number of pages | 9 |
Journal | Journal of Advanced Computational Intelligence and Intelligent Informatics |
Volume | 5 |
Issue number | 3 |
DOIs | |
Publication status | Published - 2001 May |
All Science Journal Classification (ASJC) codes
- Human-Computer Interaction
- Computer Vision and Pattern Recognition
- Artificial Intelligence