Amortized efficiency of list update and paging rules
Communications of the ACM
ACM Computing Surveys (CSUR)
Competitive paging with locality of reference
Selected papers of the 23rd annual ACM symposium on Theory of computing
Online computation and competitive analysis
Online computation and competitive analysis
Best-fit bin-packing with random order
Proceedings of the seventh annual ACM-SIAM symposium on Discrete algorithms
Bounding the diffuse adversary
Proceedings of the ninth annual ACM-SIAM symposium on Discrete algorithms
On the competitiveness of the move-to-front rule
Theoretical Computer Science
On-line paging against adversarially biased random inputs
Journal of Algorithms
The working set model for program behavior
Communications of the ACM
Operating System Concepts
The Accommodating Function: A Generalization of the Competitive Ratio
SIAM Journal on Computing
SIAM Journal on Computing
SIAM Journal on Computing
Developments from a June 1996 seminar on Online algorithms: the state of the art
Developments from a June 1996 seminar on Online algorithms: the state of the art
On paging with locality of reference
Journal of Computer and System Sciences
On adequate performance measures for paging
Proceedings of the thirty-eighth annual ACM symposium on Theory of computing
The relative worst-order ratio applied to paging
Journal of Computer and System Sciences
On the separation and equivalence of paging strategies
SODA '07 Proceedings of the eighteenth annual ACM-SIAM symposium on Discrete algorithms
List update with locality of reference
LATIN'08 Proceedings of the 8th Latin American conference on Theoretical informatics
A simpler analysis of burrows-wheeler based compression
CPM'06 Proceedings of the 17th Annual conference on Combinatorial Pattern Matching
Theoretical evidence for the superiority of LRU-2 over LRU for the paging problem
WAOA'06 Proceedings of the 4th international conference on Approximation and Online Algorithms
Introduction to the SIGACT news online algorithms column
ACM SIGACT News
On Developing New Models, with Paging as a Case Study
ACM SIGACT News
Outperforming LRU via competitive analysis on parametrized inputs for paging
Proceedings of the twenty-third annual ACM-SIAM symposium on Discrete Algorithms
Parameterized analysis of paging and list update algorithms
WAOA'09 Proceedings of the 7th international conference on Approximation and Online Algorithms
On the absolute approximation ratio for First Fit and related results
Discrete Applied Mathematics
Paging and list update under bijective analysis
Journal of the ACM (JACM)
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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, Dorrigiv and 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 Least-Recently-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 algorithms.