List Update with Locality of Reference

Angelopoulos, Spyros; Dorrigiv, Reza; López-Ortíz, Alejandro

doi:10.1007/978-3-540-78773-0_35

Cited by 24 publications

(23 citation statements)

References 19 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The question whether the same conclusions can be drawn under the substantially more stringent measure of bijective analysis was left open in [4] and [5]. In this paper we answer the question in the affirmative (c.f.…”

Section: Definition 1 ([4]mentioning

confidence: 72%

“…Indeed, [4] shows that LRU is shown to be strictly better than any online algorithm, according to average analysis on sequences which exhibit some locality of reference. Similarly, [5] shows that all list update algorithms are equivalent according to bijective analysis, in the modified-cost model, however, if the requests exhibit locality of reference, MTF is strictly better than any other algorithm under average analysis.…”

Section: Definition 1 ([4]mentioning

confidence: 92%

“…Experiments over the Calgary Compression Corpus [37], which is a standard benchmark for file compression, show [5] that the maximum number of distinct characters in a window of size w can be approximated by a function concave in w: in other words, the model of Albers et al [1] can faithfully represent locality of reference in list accessing, at least in applications which arise from file compression. Similar to the paging problem, we will denote by I f n the set of requests sequences (sequence of item accesses) which are consistent with a given concave function f .…”

Section: Preliminaries and Related Workmentioning

confidence: 99%

“…Instead, they proposed a different cost formulation (to which, following [5] we refer as the modified-cost model) and showed that every online algorithm has asymptotically the same, non-constant competitive ratio, namely Θ(l/log l), where l is the size of the list. In other words, competitive analysis is met with the same, if not worse, inadequacy in evaluating the performance of algorithms.…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Paging and list update under bijective analysis

Angelopoulos

Schweitzer

2013

J. ACM

Self Cite

View full text Add to dashboard Cite

It has long been known that for the paging problem in its standard form, competitive analysis cannot adequately distinguish algorithms based on their performance: there exists a vast class of algorithms which achieve the same competitive ratio, ranging from extremely naive and inefficient strategies (such as Flush-When-Full), to strategies of excellent performance in practice (such as Least-Recently-Used and some of its variants). A similar situation arises in the list update problem: in particular, under the cost formulation studied by Martínez and Roura [TCS 2000] and Munro [ESA 2000] every list update algorithm has, asymptotically, the same competitive ratio. Several refinements of competitive analysis, as well as alternative performance measures have been introduced in the literature, with varying degrees of success in narrowing this disconnect between theoretical analysis and empirical evaluation.In this paper we study these two fundamental online problems under the framework of bijective analysis [Angelopoulos, López-Ortiz, SODA 2007 and LATIN 2008]. This is an intuitive technique which is based on pairwise comparison of the costs incurred by two algorithms on sets of request sequences of the same size. Coupled with a well-established model of locality of reference due to Albers, Favrholdt and Giel [JCSS 2005], we show that LeastRecently-Used and Move-to-Front are the unique optimal algorithms for paging and list update, respectively. Prior to this work, only measures based on average-cost analysis have separated LRU and MTF from all other algorithms. Given that bijective analysis is a fairly stringent measure (and also subsumes average-cost analysis), we prove that in a strong sense LRU and MTF stand out as the best (deterministic) algorithms.

show abstract

Section: Definition 1 ([4]mentioning

confidence: 72%

Section: Definition 1 ([4]mentioning

confidence: 92%

Section: Preliminaries and Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Paging and list update under bijective analysis

Angelopoulos

Schweitzer

2013

J. ACM

Self Cite

View full text Add to dashboard Cite

show abstract

“…Newer work obtains separations between list accessing algorithms by analyzing these with respect to some measure of locality of reference [2,6,15].…”

Section: Introductionmentioning

confidence: 99%

List Factoring and Relative Worst Order Analysis

Ehmsen

Kohrt

Larsen

2011

Approximation and Online Algorithms

View full text Add to dashboard Cite

Relative worst order analysis is a supplement or alternative to competitive analysis which has been shown to give results more in accordance with observed behavior of online algorithms for a range of different online problems. The contribution of this paper is twofold. First, it adds the static list accessing problem to the collection of online problems where relative worst order analysis gives better results. Second, and maybe more interesting, it adds the non-trivial supplementary proof technique of list factoring to the theoretical toolbox for relative worst order analysis.

show abstract

Online List Update

Kamali¹

2016

Encyclopedia of Algorithms

View full text Add to dashboard Cite

List Update with Locality of Reference

Abstract: Abstract. It is known that in practice, request sequences for the list update problem exhibit a certain degree of locality of reference. Motivated by this observation we apply the locality of reference model for the paging problem due to Albers et al. [STOC 2002/JCSS 2005

Cited by 24 publications

References 19 publications

Paging and list update under bijective analysis

Paging and list update under bijective analysis

List Factoring and Relative Worst Order Analysis

Online List Update

Contact Info

Product

Resources

About