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17733 Resource dimensioning through buffer sampling
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Mandjes, M.R.H. and van de Meent, R. (2009) Resource dimensioning through buffer sampling. IEEE/ACM transactions on networking, 17 (5). pp. 1631-1644. ISSN 1063-6692 *** ISI Impact 2,033 ***

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Official URL: http://dx.doi.org/10.1109/TNET.2008.2009989

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Abstract

Link dimensioning, i.e., selecting a (minimal) link capacity such that the users’ performance requirements are met, is a crucial component of network design. It requires insight into the interrelationship among the traffic offered (in terms of the mean offered load , but also its fluctuation around the mean, i.e., ‘burstiness’), the envisioned performance level, and the capacity needed. We first derive, for different performance criteria, theoretical dimensioning formulas that estimate the required capacity $c$ as a function of the input traffic and the performance target. For the special case of Gaussian input traffic, these formulas reduce to $c = M + \alpha V$, where directly relates to the performance requirement (as agreed upon in a service level agreement) and $V$ reflects the burstiness (at the timescale of interest). We also observe that Gaussianity applies for virtually all realistic scenarios; notably, already for a relatively low aggregation level, the Gaussianity assumption is justified.
As estimating $M$ is relatively straightforward, the remaining open issue concerns the estimation of $V$. We argue that particularly if corresponds to small time-scales, it may be inaccurate to estimate it directly from the traffic traces. Therefore, we propose an indirect method that samples the buffer content, estimates the buffer content distribution, and ‘inverts’ this to the variance. We validate the inversion through extensive numerical experiments (using a sizeable collection of traffic traces from various representative locations); the resulting estimate of $V$ is then inserted in the dimensioning formula. These experiments show that both the inversion and the dimensioning formula are remarkably accurate.

Item Type:Article
Research Group:EWI-DACS: Design and Analysis of Communication Systems, EWI-SOR: Stochastic Operations Research
Research Program:CTIT-DSN: Dependable Systems and Networks
Research Project:EMANICS: European Network of Excellence for the Management of Internet Technologies and Complex Services
Uncontrolled Keywords:Buffer sampling, Gaussian traffic, Inversion, Large
deviations, Network dimensioning, Quality-of-service.
ID Code:17733
Status:Published
Deposited On:30 March 2010
Refereed:Yes
International:Yes
ISI Impact Factor:2,033
More Information:statisticsmetis

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