| Abstract |
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In this paper we propose a traffic predictor based on
multiresolution decomposition for the adaptive bandwidth
control in locally controlled self-sizing networks. A selfsizing
network can provide quantitative packet-level QoS
to aggregate traffic by allocating link/switch capacity automatically
and adaptively using online traffic data. In a
locally controlled network such as Internet, resource allocation
decisions are made at the node level. We show
that wavelet based adaptive bandwidth control method performs
better than other popular methods like Gaussian predictor
for such applications. We have compared the performance
of different ortho-normal wavelets and found that
Haar wavelet is best suited for traffic prediction. We have
studied the effect of other wavelet parameters such as size of
the window and number of filter coefficients. We also propose
a novel adaptive wavelet predictor which can adapt
very well to the changes of incoming bursty traffic.
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 Additional Information
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Citation:
Srikant Nalatwad, Michael Devetsikiotis,
"A Framework for Adaptive Wavelet Prediction in Self-Sizing Networks,"
anss,
pp. 10-17,
39th Annual Simulation Symposium (ANSS'06),
2006
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