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A New Perspective on List Update: Probabilistic Locality and Working Set

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Approximation and Online Algorithms (WAOA 2011)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 7164))

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Abstract

In this paper we study the performance of list update algorithms under arbitrary distributions that exhibit strict locality of reference and prove that Move-to-Front (MTF) is the best list update algorithm under any such distribution. Furthermore, we study the working set property of online list update algorithms. The working set property indicates the good performance of an online algorithm on sequences with locality of reference. We show that no list update algorithm has the working set property. Nevertheless, we can distinguish among list update algorithms by comparing their performance in terms of the working set bound. We prove bounds for several well known list update algorithms and conclude that MTF attains the best performance in this context as well.

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Authors and Affiliations

  1. Cheriton School of Computer Science, University of Waterloo, Waterloo, Ont., N2L 3G1, Canada

    Reza Dorrigiv & Alejandro López-Ortiz

Authors
  1. Reza Dorrigiv
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  2. Alejandro López-Ortiz
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Editor information

Editors and Affiliations

  1. Department of Computer Science, The University of Western Ontario, N6A 5B7, London, ON, Canada

    Roberto Solis-Oba

  2. Dipartimento di Informatica "Renato M. Capocelli", Università di Salerno, Via Ponte Don Melillo, 84081, Fisciano, SA, Italy

    Giuseppe Persiano

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Dorrigiv, R., López-Ortiz, A. (2012). A New Perspective on List Update: Probabilistic Locality and Working Set. In: Solis-Oba, R., Persiano, G. (eds) Approximation and Online Algorithms. WAOA 2011. Lecture Notes in Computer Science, vol 7164. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-29116-6_13

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  • DOI: https://doi.org/10.1007/978-3-642-29116-6_13

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-29115-9

  • Online ISBN: 978-3-642-29116-6

  • eBook Packages: Computer ScienceComputer Science (R0)

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