NDLI: Offered-load model for Pareto inter-arrival network traffic

Content Provider	IEEE Xplore Digital Library
Author	Singhai, R. Joshi, S.D. Bhatt, R.
Copyright Year	2009
Description	Author affiliation: Department of Electrical Engineering, IIT Delhi, New Delhi, India (Singhai, R.; Joshi, S.D.; Bhatt, R.)
Abstract	In the network data, interarrival times are heavy-tail distributed. Weibull, Pareto and Lognormal are the best examples of heavy-tail distributions. These distributions give time-varying arrival rates. Also, renewal processes for these interarrivals are Non-homogeneous Poisson processes. The network traffic is, thus, non-stationary. Many of the theoretical tools, such as equilibrium probabilities for Markov chains, matrix geometric solutions and Laplace transforms are not available for queue with time varying rates. Since no closed form expressions of Laplace transform of Weibull, Pareto and Lognormal distributions are available, the queueing analysis becomes complicated. In this paper we present queueing analysis of heavy-tail network traffic, and thus, time dependent queuing model Mt/G/∞ is analyzed for heavy-tail Pareto arrivals. In particular, we find analytical expressions for the time-dependent mean function (offered load), denoted here as m(t), which depends on the time-dependent arrival rate function λ(t) and service time distribution.
Starting Page	364
Ending Page	367
File Size	276377
Page Count	4
File Format	PDF
ISBN	9781424444885
ISSN	07421303
DOI	10.1109/LCN.2009.5355115
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2009-10-20
Publisher Place	Switzerland
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Telecommunication traffic Traffic control Queueing analysis Pareto analysis Probability distribution Load modeling Distribution functions Computer networks Laplace equations Region 6 Pareto distribution Network traffic Poisson process Non-homogeneous Poisson process Long-range dependence Self-similarity Heavy-tail distributions time-varying arrival rates Stationary Excess distribution Pointwise stationary approximations (PSA)
Content Type	Text
Resource Type	Article

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