List update with probabilistic locality of reference

Dorrigiv, Reza; López-Ortíz, Alejandro

doi:10.1016/j.ipl.2012.04.002

Cited by 4 publications

(3 citation statements)

References 21 publications

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“…The results of Albers and Lauer [4] confirm, in a theoretical manner, that MTF has excellent performance at high locality. Later studies of the impact of locality of reference in list update can be found in [33,31].…”

Section: List Update With Locality Of Referencementioning

confidence: 99%

On the Separation and Equivalence of Paging Strategies and Other Online Algorithms

2018

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We introduce a new technique for the analysis of online algorithms, namely bijective analysis, that is based on pair-wise comparison of the costs incurred by the algorithms. Under this framework, an algorithm A is no worse than an algorithm B if there is a bijection π defined over all request sequences of a given size such that the cost of A on σ is no more than the cost of B on B(π(σ)). We also study a relaxation of bijective analysis, termed average analysis, in which we compare two algorithms based on their corresponding average costs over request sequences of a given size. We apply these new techniques in the context of two fundamental online problems, namely paging and list update. For paging, we show that any two lazy online algorithms are equivalent under bijective analysis. This result demonstrates that, without further assumptions on characteristics of request sequences, it is unlikely, or even undesirable, to separate online paging algorithms based on their performance. However, once we restrict the set of request sequences to those exhibiting locality of reference, and in particular using a model of locality due to Albers, Favrholdt, and Giel [JCSS 2005], we demonstrate that Least-Recently-Used (LRU) is the unique optimal strategy according to average analysis. This is, to our knowledge, the first deterministic model to provide full theoretical backing to the empirical observation that LRU is preferable in practice. Concerning list update, we obtain similar conclusions, in terms of the bijective comparison of any two online algorithms, and in terms of the superiority (albeit not necessarily unique) of the Move-To-Front (MTF) heuristic in the presence of locality of reference.

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Section: List Update With Locality Of Referencementioning

confidence: 99%

On the Separation and Equivalence of Paging Strategies and Other Online Algorithms

2018

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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%

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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Alternative Performance Measures in Online Algorithms

López-Ortíz¹

2016

Encyclopedia of Algorithms

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List update with probabilistic locality of reference

Cited by 4 publications

References 21 publications

On the Separation and Equivalence of Paging Strategies and Other Online Algorithms

On the Separation and Equivalence of Paging Strategies and Other Online Algorithms

On list update with locality of reference

Alternative Performance Measures in Online Algorithms

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