Over the last few years, the network community has started to rely heavily on the use of novel concepts such as self-similarity 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. We find 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. The goal of this software tool is to develop a systematic approach for measurement analysis and characterization.
Current Results
We arrive at several conclusions that could provide quidelines for a systematic approach to LRD.
First, long-range dependence may exist even, if the estimators have different estimates in value.
Second, long-range dependence is unlikely to exist, if there are several estimators that do not ``converge'' statistically to a value.
Third, we show that periodicity can obscure the analysis of a signal giving partial evidence of long range 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.
To download: this is the site for the tool.