att_abstract={{Heavy tails in work loads (file sizes, flow lengths,
service times, etc.) have significant negative impact on the
performance of queues and networks. In the context of the
famous Internet file size data of Crovella and some very recent
data sets from a wireless mobility network, we examine the
new class of LogPH distributions introduced by Ramaswami for
modeling heavy tailed random variables. The fits obtained are
validated using separate training and test data sets, and also in
terms of the ability of the model to predict performance measures
accurately as compared with a trace driven simulation using NS2
of a bottleneck Internet link running a TCP protocol. Use of the
LogPH class is motivated by the fact that these distributions have
a power law tail and can approximate any distribution arbitrarily
closely not just in the tail but in its entire range. In many practical
contexts, although the tail exerts significant effect on performance
measures, nevertheless the bulk of the data is in the head of the
distribution. Our results based on a comparison of the LogPH fit
with other classical model fits like Pareto, Weibull, Lognormal,
and Log-t demonstrate the greater accuracy achievable by the
use of LogPH distributions and also confirm the importance of
modeling the distribution in its entire range and not just in the
	att_authors={vr1945, rj2124, va037f},
	att_copyright_notice={{The definitive version was published in 2013. {{, 2013-12-19}}{{, http://www.springer.com/series/4240}}
	att_tags={heavy tail,  LogPH,  mobility},
	author={Vaidyanathan Ramaswami and Rittwik Jana and Vaneet Aggarwal and Kaustubh Jain},
	institution={{Springer - Advances in Intelligent and Soft Computing}},
	title={{Modeling Heavy-tails in Traffic Sources for Network Performance Evaluation}},