Dynamic Neural-Based Buffer Management for Queuing Systems With Self-Similar Characteristics

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.