A comparative study of fuzzy versus "fixed" thresholds for robust queue management in cell-switching networks

• Published in: IEEE/ACM transactions on networking Vol. 2; no. 4; pp. 337 - 344
• Main Authors: Bonde, A.R., Ghosh, S.
• Format: Journal Article
• Language: English
• Published: IEEE 01.08.1994
• Subjects: Admission control; Communication networks; Communication system traffic control; Computational modeling; Fuzzy set theory; Propagation delay; Smoothing methods; Switches; Traffic control; Waste materials
• ISSN: 1063-6692
• DOI: 10.1109/90.330414

High-performance cell-based communications networks have been conceived to carry asynchronous traffic sources and support a continuum of transport rates ranging from low bit-rate to high bit-rate traffic. When a number of bursty traffic sources add cells, the network is inevitably subject to congestion. Traditional approaches to congestion management include admission control algorithms, smoothing functions, and the use of finite-sized buffers with queue management techniques. Most queue management schemes, reported in the literature, utilize "fixed" thresholds to determine when to permit or refuse entry of cells into the buffer. The aim is to achieve a desired tradeoff between the number of cells carried through the network, propagation delays of the cells, and the number of discarded cells. While binary thresholds are excessively restrictive, the rationale underlying the use of a large number of priorities appears to be ad hoc, unnatural, and unclear. The paper introduces the notion of cell-blocking, wherein a fuzzy thresholding function, based on Zadeh's (1965) fuzzy set theory, is utilized to deliberately refuse entry to a fraction of incoming cells from other switches. The blocked cells must be rerouted by the sending switch to other switches and, in the process, they may incur delays. The fraction of blocked cells is a continuous function of the current buffer occupancy level unlike the abrupt. The fuzzy cell-blocking scheme is simulated on a computer. Fuzzy queue management adapts superbly to sharp changes in cell arrival rates and maximum burstiness of bursty traffic sources.< >