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

Dorrigiv, Reza; López-Ortíz, Alejandro

doi:10.1007/978-3-642-29116-6_13

Cited by 2 publications

(2 citation statements)

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“…Subsequent work: After the conference publication of our paper, list update with locality of reference has been studied in [19,22,21]. Dorrigiv et al [19] propose a locality model based on Denning's working sets and analyze the performance of algorithms using a certain non-locality parameter.…”

Section: Introductionmentioning

confidence: 99%

“…However, no experimental study is conducted, comparing the theoretical bounds to those observed in practice. Dorrigiv and López-Ortiz [22,21] study list update algorithms under probability distributions that exhibit locality of reference. They analyze the expected service cost of various algorithms and show that Move-To-Front is the best strategy in this framework.…”

Section: Introductionmentioning

confidence: 99%

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On list update with locality of reference

Albers

Lauer

2016

Journal of Computer and System Sciences

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We present a comprehensive study of the list update problem with locality of reference. More specifically, we present a combined theoretical and experimental study in which the theoretically proven and experimentally observed performance guarantees of algorithms match or nearly match. In the first part of the paper we introduce a new model of locality of reference that closely captures the concept of runs, representing sequences of requests to the same item. Using this model we develop refined theoretical analyses of popular list update algorithms. The second part of the paper is devoted to an extensive experimental study in which we have tested the algorithms on traces from benchmark libraries. It shows that the theoretical and experimental bounds differ by just a few percent. Our new theoretical bounds are substantially lower than those provided by standard competitive analysis. Another result is that the well-known Move-To-Front strategy exhibits the best performance. Its refined competitive ratio tends to 1 as the degree of locality in a request sequence increases. This confirms that Move-To-Front is the method of choice in practice.

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