NDLI: Heavy-tailed on/off source behavior and self-similar traffic

Content Provider	IEEE Xplore Digital Library
Author	Pruthi, P. Erramilli, A.
Copyright Year	1995
Description	Author affiliation: R. Inst. of Technol., Stockholm, Sweden (Pruthi, P.)
Abstract	Traffic measurement studies suggest that the self-similarity observed in packet traffic arises from aggregating individual sources which behave in an on/off manner with heavy-tailed sojourn times in one or both of the states. We investigate the connection between general on/off behavior, self-similarity and queueing performance. We use chaotic maps to model general on/off behavior with combinations of heavy tailed and light tailed sojourn time behavior. We present results which show that chaotic maps which capture the heavy-tailed sojourn time behavior in the off and/or on states generate traffic that is asymptotically self-similar. However, the resulting queue length distribution decays as a power law with the heavy on source, and as an exponential with the light on source, even though both processes exhibit identical l/f noise behavior. To resolve this apparent paradox, we consider aggregates of on and off sources, and show that the nature of the on period is less consequential, and in both instances the aggregate appears to converge to fractional Brownian motion (FBM). The queueing behavior is heavy in both cases, corresponding to the "stretched exponential" form predicted by FBM models. This indicates that differences in the single source case arise due to the impacts of higher order statistics, which become less significant as sources are aggregated. Convergence to FBM is observed to be slower with light on sources. Our results indicate that in assessing the impact of long range dependence on performance, the potential impacts of other factors, such as higher-order statistics, must also be considered. Our analysis indicates conditions under which long-range dependence can dominate queueing performance in fast packet and SS7 networks, and with variable bit rate (VBR) video applications.
Starting Page	445
Ending Page	450
File Size	737239
Page Count	6
File Format	PDF
ISBN	0780324862
DOI	10.1109/ICC.1995.525209
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	1995-06-18
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Traffic control Chaos Aggregates Higher order statistics 1f noise Brownian motion Predictive models Convergence Performance analysis Queueing analysis
Content Type	Text
Resource Type	Article

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