A large deviation analysis of errors in measurement based admission control to buffered and bufferless resources Academic Article

abstract

• In measurement based admission control, measured traffic parameters are used to determine the maximum number of connections that can be admitted to a resource within a given quality constraint. The assumption that the measured parameters are the true ones can compromise admission control; measured parameters are random quantities, causing additional variability. This paper analyzes the impact of measurement error within the framework of Large Deviation theory. For a class of admission controls, large deviation principles are established for the number of admitted connections, and for the attained overflow rates. These are applied to admission to bufferless resources, and buffered resources in both the many sources and large buffer asymptotic. The sampling properties of effective bandwidths are presented, together with a discussion of the impact of the temporal extent of individual samples on estimator variability. Sample correlations are shown to increase estimator variance; procedures to make admission control robust with respect to these are described.

authors

• Duffield, Nick

published proceedings

• QUEUEING SYSTEMS

author list (cited authors)

• Duffield, N. G.

citation count

• 12

complete list of authors

• Duffield, NG

publication date

• March 2000

publisher

• Springer Nature  Publisher

published in

• Queueing Systems  Journal

keywords

• Effective Bandwidths
• Estimation
• Large-buffer Asymptotics
• Many-sources Asymptotics
• Markov Processes
• Overflow Probabilities
• Sampling Errors

Digital Object Identifier (DOI)

• 10.1023/A:1019152918746

start page

• 131

end page

• 168

volume

• 34

issue

• 1-4

URL

• http://dx.doi.org/10.1023/a:1019152918746