Title
Improved Estimation Of The Hurst Parameter Of Long-Range Dependent Traffic Using The Modified Hadamard Variance
Abstract
Internet traffic exhibits self-similarity and long-range dependence (LRD) on various time scales. In this paper, we propose to use the Modified Hadamard Variance (MHVAR), a time-domain measure for high-resolution spectral analysis, to estimate the Hurst parameter H of LRD traffic data series or, more generally, the exponent a of traffic series with 1/f(alpha) power-law spectrum. MHVAR generalizes the principle of the Modified Allan Variance (MAVAR), a well-known tool widely used since 1981 for frequency stability characterization, to higher-order differences of input data; in our knowledge, it has been mentioned in literature only few times and with little detail so far.The behaviour of MHVAR with power-law random processes and some common deterministic signals (viz. drifts, sine waves, steps) is studied. The MHVAR performance in estimating H is evaluated by analysis and simulation, comparing it to the wavelet Logscale Diagram (LD) and to MAVAR. Extensive simulations show that MHVAR has highest accuracy and confidence in fractional-noise parameter estimation, even slightly better than MAVAR. Moreover, MHVAR features a number of other advantages, which make it useful to complement other established techniques such as MAVAR and LD. Finally, MHVAR and LD are also applied to a real IP traffic trace.
Year
DOI
Venue
2006
10.1109/ICC.2006.254855
2006 IEEE INTERNATIONAL CONFERENCE ON COMMUNICATIONS, VOLS 1-12
Keywords
Field
DocType
Fractional Brownian motion, fractional noise, Internet, long-range dependence, random walk, self-similarity, traffic control (communication), traffic model
Random walk,Hurst exponent,Algorithm,Stochastic process,Real-time computing,Modified Allan variance,Estimation theory,Statistics,Hadamard transform,Self-similarity,Mathematics,Wavelet
Conference
ISSN
Citations 
PageRank 
1550-3607
7
0.74
References 
Authors
5
2
Name
Order
Citations
PageRank
Stefano Bregni17710.52
Luca Jmoda270.74