Dynamic neural-based buffer management for queuing systems with self-similar characteristics

Authors:
H. Yousefi'zadeh;E. A. Jonckheere
Affiliations:
Dept. of Electr. Eng. & Comput. Sci., Univ. of California, Irvine, CA, USA;-
Venue:
IEEE Transactions on Neural Networks
Year:
2005

Citing 0
Cited 7

Performance analysis of priority scheduling mechanisms under heterogeneous network traffic

Journal of Computer and System Sciences
A heuristic flow-decomposition approach for generalized processor sharing under self-similar traffic

Journal of Computer and System Sciences
Long-term time series prediction with the NARX network: An empirical evaluation

Neurocomputing
VHDL implementation of neuro-fuzzy based adaptive bandwidth controller for ATM networks

International Journal of Communication Networks and Distributed Systems
Modelling and analysis of priority queueing systems with multi-class self-similar network traffic: a novel and efficient queue-decomposition approach

IEEE Transactions on Communications
A comprehensive analytical model for weighted fair queuing under multi-class self-similar traffic

ICC'09 Proceedings of the 2009 IEEE international conference on Communications
Buffer Management in Cellular IP Networks using Evolutionary Algorithms

International Journal of Applied Evolutionary Computation

Quantified Score

Hi-index	0.00

Visualization

Abstract

Buffer management in queuing systems plays an important role in addressing the tradeoff between efficiency measured in terms of overall packet loss and fairness measured in terms of individual source packet loss. Complete partitioning (CP) of a buffer with the best fairness characteristic and complete sharing (CS) of a buffer with the best efficiency characteristic are at the opposite ends of the spectrum of buffer management techniques. Dynamic partitioning buffer management techniques aim at addressing the tradeoff between efficiency and fairness. Ease of implementation is the key issue when determining the practicality of a dynamic buffer management technique. In this paper, two novel dynamic buffer management techniques for queuing systems accommodating self-similar traffic patterns are introduced. The techniques take advantage of the adaptive learning power of perceptron neural networks when applied to arriving traffic patterns of queuing systems. Relying on the water-filling approach, our proposed techniques are capable of coping with the tradeoff between packet loss and fairness issues. Computer simulations reveal that both of the proposed techniques enjoy great efficiency and fairness characteristics as well as ease of implementation.