Long-Range Dependence: Now you see it, now you don’t!

Over the last few years, the network community has started to make heavy use of novel concepts such as selfsimilarity and Long-Range Dependence (LRD). Despite their wide use, there is still much confusion regarding the identification of such phenomena in real network traffic data. In this paper, we show that estimating Long-Range Dependence is not straightforward: there is no systematic or definitive methodology. There exist several estimating methodologies, but they can give misleading and conflicting estimates. More specifically, we arrive at several conclusions that could provide guidelines for a systematic approach to LRD. First, long-range dependence may exist even, if the estimators have different estimates of the Hurst exponent in the interval 0.5-1. Second, long-range dependence is unlikely to exist, if there are several estimators that fail to estimate the Hurst exponent. Third, we show that periodicity can obscure the analysis of a signal giving partial evidence of longrange dependence. Fourth, the Whittle estimator is the most accurate in finding the exact value when LRD exists, but it can be fooled easily by periodicity. As a case-study, we analyze real round-trip time data. We find and remove a periodic component from the signal, before we can identify long-range dependence in the remaining signal.

PDF file

In  IEEE GLOBECOM - Global Internet Symposium

Publisher  IEEE Communications Society
Copyright © 2007 IEEE. Reprinted from IEEE Communications Society. This material is posted here with permission of the IEEE. Internal or personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution must be obtained from the IEEE by writing to pubs-permissions@ieee.org. By choosing to view this document, you agree to all provisions of the copyright laws protecting it.


> Publications > Long-Range Dependence: Now you see it, now you don’t!