  In the mid-90's, it was shown that the statistics of aggregated time series
from Internet traffic departed from those of traditional short range dependent
models, and were instead characterized by asymptotic self-similarity. Following
this seminal contribution, over the years, many studies have investigated the
existence and form of scaling in Internet traffic. This contribution aims first
at presenting a methodology, combining multiscale analysis (wavelet and wavelet
leaders) and random projections (or sketches), permitting a precise, efficient
and robust characterization of scaling which is capable of seeing through
non-stationary anomalies. Second, we apply the methodology to a data set
spanning an unusually long period: 14 years, from the MAWI traffic archive,
thereby allowing an in-depth longitudinal analysis of the form, nature and
evolutions of scaling in Internet traffic, as well as network mechanisms
producing them. We also study a separate 3-day long trace to obtain
complementary insight into intra-day behavior. We find that a biscaling (two
ranges of independent scaling phenomena) regime is systematically observed:
long-range dependence over the large scales, and multifractal-like scaling over
the fine scales. We quantify the actual scaling ranges precisely, verify to
high accuracy the expected relationship between the long range dependent
parameter and the heavy tail parameter of the flow size distribution, and
relate fine scale multifractal scaling to typical IP packet inter-arrival and
to round-trip time distributions.
