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    Large Deviations Of Max-Weight Scheduling Policies On Convex Rate Regions


    Subramanian, Vijay G. (2008) Large Deviations Of Max-Weight Scheduling Policies On Convex Rate Regions. In: 2008 Information Theory and Applications Workshop (ITA 2008), 27th Janaury - 1st February 2008, University of California, San Diego.

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    Abstract

    We consider a single server discrete-time system with K users where the server picks operating points from a compact, convex and co-ordinate convex set in Rk+. For this system we analyse the performance of a stablising policy that at any given time picks operating points from the allowed rate region that maximise a weighted sum of rate, where the weights depend upon the workloads of the users. Assuming a Large Deviations Principle (LDP) for the arrival processes in the Skorohod space of functions that are right-continuous with left-hand limits we establish an LDP for the workload process using a generalised version of the contraction principle to derive the corresponding rate function. With the LDP result available we then analyse the tail probabilities of the workloads under different buffering scenarios.
    Item Type: Conference or Workshop Item (Paper)
    Keywords: Max-weight scheduling; Convex rate regions; Large Deviations Principle; Contraction principle; Continuous mapping principle; ITA 2008; Hamilton Institute.
    Academic Unit: Faculty of Science and Engineering > Computer Science
    Faculty of Science and Engineering > Research Institutes > Hamilton Institute
    Faculty of Science and Engineering > Mathematics and Statistics
    Item ID: 1651
    Depositing User: Hamilton Editor
    Date Deposited: 10 Nov 2009 10:50
    Journal or Publication Title: Proceedings of ITA
    Publisher: University of California
    Refereed: No
    Related URLs:
    URI: https://mu.eprints-hosting.org/id/eprint/1651
    Use Licence: This item is available under a Creative Commons Attribution Non Commercial Share Alike Licence (CC BY-NC-SA). Details of this licence are available here

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