Bandwidth prediction and congestion control for ABR traffic based on neural networks

Authors:
Zhixin Liu;Xinping Guan;Huihua Wu
Affiliations:
Institute of Electrical Engineering, Yanshan Univesity, Qinhuangdao, Hebei, China;Institute of Electrical Engineering, Yanshan Univesity, Qinhuangdao, Hebei, China;Institute of Electrical Engineering, Yanshan Univesity, Qinhuangdao, Hebei, China
Venue:
ISNN'06 Proceedings of the Third international conference on Advances in Neural Networks - Volume Part III
Year:
2006

Citing 2
Cited 2

The ERICA switch algorithm for ABR traffic management in ATM networks

IEEE/ACM Transactions on Networking (TON)
ABR traffic management using minimal resource allocation (neural) networks

Computer Communications

Support vector regression and ant colony optimization for grid resources prediction

ISNN'10 Proceedings of the 7th international conference on Advances in Neural Networks - Volume Part II
Genetic algorithms with improved simulated binary crossover and support vector regression for grid resources prediction

ISNN'10 Proceedings of the 7th international conference on Advances in Neural Networks - Volume Part II

Quantified Score

Hi-index	0.00

Visualization

Abstract

In this paper, queue length in the buffer is considered as a scale to evaluate the quality of service of communication network. To realize that the system is stable and avoid the data congestion under complex environments, the neural networks predictor and controller are designed, which can predict the bursty available bandwidth for ABR traffic effectively and force the queue level in the buffer to the desired region, respectively. The fairness of different connections is achieved through fair algorithm. The simulations verify the effectiveness of the proposed scheme.