Measurement-based Admission Control Using Wavelets for Broadband Networks

Abstract

The current broadband networks allow a number of widely disparate traffic streams to share common resources through statistical multiplexing. How efficiently the resource sharing can be managed depends critically upon the analysis of statistical characteristics of the traffic. Numerous analyses of traffic measurements have shown that a large variety of network traffic, in WAN as well as in LAN, exhibits self-similar bursty behavior or long-range dependency (LRD). Self-similar traffic shows structural similarities across a wide range of time-scales (milliseconds, seconds, minutes, etc) and can be characterized by just one parameter (Hurst parameter). Many studies have shown that self-similar network traffic may have a detrimental impact on network performance, including increased queuing delay and packet loss rate under traditional queuing analysis and simulations [8]. It has been shown that network traffic traces exhibit only asymptotically self-similar behavior rather than strict self-similarity. Furthermore, the multifractal nature of WAN traffic was recently revealed [5]. With multifractal nature of traffic, buffer queueing prediction may be over-optimistic even when taking LRD into consideration [4]. The implications of those complex traffic behaviors add increased difficulty to optimizing resource usage such as call admission control. We also believe that analyzing large rapi