NDLI: Bivariate gamma distribution: A plausible solution for joint distribution of packet arrival and their sizes

Content Provider	IEEE Xplore Digital Library
Author	Bhattacharjee, A. Nandi, S.
Copyright Year	2010
Description	Author affiliation: Dept. of Computer Science and Engineering, Indian Institute of Technology, Guwahati, India 781039 (Nandi, S.) \|\| Dept. of Computer Science and Engineering, National Institute of Technology, Silchar, India 788010 (Bhattacharjee, A.)
Abstract	Network traffic has been studied extensively since new findings by Taqqu et. al. (1994), which has shown that network traffic is not memoryless. Such traffic has been called self similar with Long Range Dependence (LRD) and their distribution is commonly known as heavy-tailed. It is very hard to estimate buffer size to protect against overflow in presence of such traffic as packet sizes and their arrival count is positively correlated. Queuing analysis of network devices consider only arriving packets irrespective of their sizes, but existing network protocols allow for variable packet sizes. This can lead to higher overflow probability. This paper examines network traffic heavy-tailedness assumption via number of experimentation on connectionless service traffic. Number of arrival per second and number of bytes transferred per second are found to be highly correlated across lags. Based on these findings, this work proposes bivariate gamma distribution for joint probability distribution of these parameters.
Starting Page	125
Ending Page	130
File Size	188912
Page Count	6
File Format	PDF
ISBN	9781424484966
e-ISBN	9781424484973
DOI	10.1109/ICCITECHN.2010.5723841
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2010-12-23
Publisher Place	Bangladesh
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Bivariate Gamma distribution Ethernet networks Correlation Exponential distribution Switches Heavy-tail distribution Self similarity Mean excess Queueing analysis Delay Local area networks LRD
Content Type	Text
Resource Type	Article

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