Abstract:
Traffic Matrix Estimation (TME) techniques address the problem of determination
of a network’s traffic demand matrix from its link load measurements and is
considered critical for capacity planning, anomaly detection and many other network
management related tasks. With the advent of cloud services such as IaaS (Infrastructure
as a Service), Paas (Platform as a Service) and Saas (Software as a Service),
the traffic patterns are difficult to model since they do not follow a single probability
distribution such as Poisson, Gaussian, Negative Binomial etc., thus decreasing the
estimation accuracy using the available methods.
Traffic Matrix Estimation for a large network with accuracy is of utmost importance
and is considered a challenging problem. Many approaches use statistical
inference distribution on traffic matrix elements that rely on initial or available measurements
of the traffic flow (mean and variance). This thesis asserts and proposes
a solution for the estimation of traffic matrix that possibly exhibits over-dispersion,
which is a more severe problem with mice flows (i.e. small flows) than the elephant
flows (i.e. large flows). Moreover, this thesis presents a traffic matrix estimator
which shows optimal performance while minimizing errors when there are sparse and
limited measurements(training datasets) availability. Furthermore, this thesis inves-
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tigates the effects of sparsity and measurement errors (training data errors) for a
large network.
The main contribution of this thesis are 1) investigation for the traffic matrix
that may experience over-dispersion and formulation of a two-step optimization
approach with appropriate accuracy and additional constraint. 2) Investigate and
development of a novel architecture that demonstrates superior outcomes for simulations
for real datasets and 3) review the case of traffic matrix estimation in which
the measurements (training datasets) may be limited in size and may have missing
information or incomplete data with errors
