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

  • Reza Dorrigiv
  • Alejandro López-Ortiz
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7164)

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.

Keywords

Competitive Ratio Probabilistic Locality Online Algorithm Static List High Locality 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Reza Dorrigiv
    • 1
  • Alejandro López-Ortiz
    • 1
  1. 1.Cheriton School of Computer ScienceUniversity of WaterlooWaterlooCanada

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