Learning traffic correlations in multi-class queueing systems by sampling queue lengths, with routing applications

Martin Zubeldia; Michel Mandjes

doi:10.1145/3529113.3529130

Learning traffic correlations in multi-class queueing systems by sampling queue lengths, with routing applications

Martin Zubeldia, Michel Mandjes

Research output: Contribution to journal › Article › peer-review

1 Scopus citations

Abstract

We consider a system of parallel single-server queues. Work of different classes arrives as correlated Gaussian processes with known drifts but unknown covariance matrix, and it is deterministically routed to the different queues according to some routing matrix.

Original language	English (US)
Pages (from-to)	53-54
Number of pages	2
Journal	Performance Evaluation Review
Volume	49
Issue number	3
DOIs	https://doi.org/10.1145/3529113.3529130
State	Published - Dec 2021
Externally published	Yes

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Publisher Copyright:
© 2022 Copyright is held by the owner/author(s).

Keywords

gaussian processes
indirect learning
large deviations
queueing systems

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10.1145/3529113.3529130

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title = "Learning traffic correlations in multi-class queueing systems by sampling queue lengths, with routing applications",

abstract = "We consider a system of parallel single-server queues. Work of different classes arrives as correlated Gaussian processes with known drifts but unknown covariance matrix, and it is deterministically routed to the different queues according to some routing matrix.",

keywords = "gaussian processes, indirect learning, large deviations, queueing systems",

author = "Martin Zubeldia and Michel Mandjes",

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volume = "49",

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KW - gaussian processes

KW - indirect learning

KW - large deviations

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Learning traffic correlations in multi-class queueing systems by sampling queue lengths, with routing applications

Abstract

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