id: 05161271
dt: j
an: 05161271
au: Masugi, Masao
ti: Multi-fractal analysis of IP-network traffic based on a hierarchical
    clustering approach.
so: Commun. Nonlinear Sci. Numer. Simul. 12, No. 7, 1316-1325 (2007).
py: 2007
pu: Elsevier Science, Amsterdam
la: EN
cc: 
ut: network traffic; self-similarity; multi-fractal; hierarchical clustering
ci: 
li: doi:10.1016/j.cnsns.2005.12.004
ab: Summary: This paper describes an analysis of IP-network traffic in terms of
    the time variations in multi-fractal scaling properties. To obtain a
    comprehensive view in assessing IP-network traffic conditions, we used
    a hierarchical clustering scheme, which provides a way to classify
    high-dimensional data into a tree-like structure. Based on sequential
    measurements of IP-network traffic at two locations, we checked time
    variations in multi-fractal-related properties of measured data sets.
    In performing the hierarchical clustering-based analysis, we used four
    parameters: the highest value and the range of generalized fractal
    dimensions, the network throughput, and the standard deviation of
    average throughput for each measured data set. The results confirmed
    that the traffic data could be classified in accordance with the
    network traffic properties, demonstrating that the combined depiction
    of the multi-fractal-related properties and other factors can give us
    an effective assessment of network conditions at different times.
rv: