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Paul Tune
Researcher at University of Adelaide
Publications - 27
Citations - 448
Paul Tune is an academic researcher from University of Adelaide. The author has contributed to research in topics: Network planning and design & Sampling (statistics). The author has an hindex of 11, co-authored 27 publications receiving 399 citations. Previous affiliations of Paul Tune include University of Melbourne.
Papers
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Internet Traffic Matrices: A Primer
Paul Tune,Matthew Roughan +1 more
TL;DR: This chapter explores the various issues involved in measuring and characterising traffic matrices, and summarises open questions in Internet traffic matrix research, providing a list of resources useful for the researcher and practitioner.
Proceedings ArticleDOI
Towards optimal sampling for flow size estimation
Paul Tune,Darryl Veitch +1 more
TL;DR: Dual Sampling is presented, which can to a large extent provide flow-sampling-like statistical performance for packet-samplings-like computational cost for TCP flows and it is shown how DS significantly outperforms other packet based methods, but also proves that DS is inferior to flow sampling.
Proceedings ArticleDOI
Spatiotemporal Traffic Matrix Synthesis
Paul Tune,Matthew Roughan +1 more
TL;DR: It is shown how the principle of maximum entropy can be used to generate a wide variety of traffic matrices constrained by the needs of a particular task, and the available information, but otherwise avoiding hidden assumptions about the data.
Proceedings ArticleDOI
Downlink scheduling using compressed sensing
TL;DR: A distributed self-selection procedure is combined with the technique of compressed sensing to identify a set of users who are getting simultaneous access to the downlink broadcast channel.
Journal ArticleDOI
Fisher Information in Flow Size Distribution Estimation
Paul Tune,Darryl Veitch +1 more
TL;DR: This work presents dual sampling (DS), a two-parameter family, which, to a large extent, provide FS-like statistical performance by approaching FS continuously, with just packet-sampling-like computational cost.