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Prediction of end-to-end single flow characteristics in best-effort networks

The nature of user traffic in coming years will become increasingly multimediaoriented
which has much more stringent Quality of Service (QoS) requirements. The
current generation of the public Internet does not provide any strict QoS guarantees.
Providing Quality of Service (QoS) for multimedia application has been a difficult
and challenging problem. Developing predictive models for best-effort networks, like
the Internet, would be beneficial for addressing a number of technical issues, such as
network bandwidth provisioning, congestion avoidance/control to name a few. The
immediate motivation for creating predictive models is to improve the QoS perceived
by end-users in real-time applications, such as audio and video.
This research aims at developing models for single-step-ahead and multi-stepahead
prediction of end-to-end single flow characteristics in best-effort networks.
The performance of path-independent predictors has also been studied in this research.
Empirical predictors are developed using simulated traffic data obtained
from ns-2 as well as for actual traffic data collected from PlanetLab. The linear system
identification models Auto-Regressive (AR), Auto-Regressive Moving Average
(ARMA) and the non-linear models Feed-forward Multi-layer Perceptron (FMLP)
have been used to develop predictive models. In the present research, accumulation
is chosen as a signal to model the end-to-end single flow characteristics. As the raw
accumulation signal is extremely noisy, the moving average of the accumulation isused for the prediction. Developed predictors have been found to perform accurate
single-step-ahead predictions. However, as the multi-step-ahead prediction horizon is
increased, the models do not perform as accurately as in the single-step-ahead prediction
case. Acceptable multi-step-ahead predictors for up to 240 msec prediction
horizon have been obtained using actual traffic data.
Date29 August 2005
CreatorsShukla, Yashkumar Dipakkumar
ContributorsParlos, Alexander
PublisherTexas A&M University
Source SetsTexas A and M University
Detected LanguageEnglish
TypeElectronic Thesis, text
Format1191782 bytes, electronic, application/pdf, born digital

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