Journal Article•
On ~ competitive algorithms for paging problems
About: This article is published in Journal of Algorithms.The article was published on 1991-01-01 and is currently open access. It has received 37 citations till now. The article focuses on the topics: Paging.
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TL;DR: The partitioning algorithm is developed, a randomized on-line algorithm for the paging problem, which it is proved that its expected cost on any sequence of requests is within a factor ofHk of optimum.
Abstract: Thepaging problem is that of deciding which pages to keep in a memory ofk pages in order to minimize the number of page faults. We develop thepartitioning algorithm, a randomized on-line algorithm for the paging problem. We prove that its expected cost on any sequence of requests is within a factor ofH
k of optimum. (H
k is thekth harmonic number, which is about ln(k).) No on-line algorithm can perform better by this measure. Our result improves by a factor of two the best previous algorithm.
247 citations
01 Sep 1991
TL;DR: It is shown for powerful models and mth order Markov sources that the page fault rates incurred by the prefetching algorithms presented are optimal in the limit for almost all sequences of page accesses.
Abstract: A form of the competitive philosophy is applied to the problem of prefetching to develop an optimal universal prefetcher in terms of fault ratio, with particular applications to large-scale databases and hypertext systems. The algorithms are novel in that they are based on data compression techniques that are both theoretically optimal and good in practice. Intuitively, in order to compress data effectively, one has to be able to predict feature data well, and thus good data compressors should be able to predict well for purposes of prefetching. It is shown for powerful models such as Markov sources and mth order Markov sources that the page fault rates incurred by the prefetching algorithms presented are optimal in the limit for almost all sequences of page accesses. >
208 citations
03 Jan 1991
TL;DR: A graph-theoretical model, the access graph, is devised for studying locality of reference and is used to address the following questions: How well is LRU likely to perform on a given program?
Abstract: The Sleator-Tarjan competitive analysis of paging (Comm. ACM28 (1985), 202-208) gives us the ability to make strong theoretical statements about the performance of paging algorithms without making probabilistic assumptions on the input. Nevertheless practitioners voice reservations about the model, citing its inability to discern between LRU and FIFO (algorithms whose performances differ markedly in practice), and the fact that the theoretical comptitiveness of LRU is much larger than observed in practice, In addition, we would like to address the following important question: given some knowledge of a program?s reference pattern, can we use it to improve paging performance on that program? We address these concerns by introducing an important practical element that underlies the philosophy behind paging: locality of reference. We devise a graph-theoretical model, the access graph, for studying locality of reference. We use it to prove results that address the practical concerns mentioned above, In addition, we use our model to address the following questions: How well is LRU likely to perform on a given program? Is there a universal paging algorithm that achieves (nearly) the best possible paging performance on every program? We do so without compromising the benefits of the Sleator-Tarjan model, while bringing it closer to practice.
190 citations
22 Oct 1990
TL;DR: Deterministic competitive k-server algorithms are given for all k and all metric spaces and the competitive ratio can be proved is exponential in the number of servers, which settles the k- server conjecture.
Abstract: Deterministic competitive k-server algorithms are given for all k and all metric spaces. This settles the k-server conjecture of M.S. Manasse et al. (1988) up to the competitive ratio. The best previous result for general metric spaces was a three-server randomized competitive algorithm and a nonconstructive proof that a deterministic three-server competitive algorithm exists. The competitive ratio the present authors can prove is exponential in the number of servers. Thus, the question of the minimal competitive ratio for arbitrary metric spaces is still open. The methods set forth here also give competitive algorithms for a natural generalization of the k-server problem, called the k-taxicab problem. >
158 citations
TL;DR: This paper applies a form of the competitive philosophy for the first time to the problem of prefetching to develop an optimal universal prefetcher in terms of fault rate, with particular applications to large-scale databases and hypertext systems.
Abstract: Caching and prefetching are important mechanisms for speeding up access time to data on secondary storage. Recent work in competitive online algorithms has uncovered several promising new algorithms for caching. In this paper, we apply a form of the competitive philosophy for the first time to the problem of prefetching to develop an optimal universal prefetcher in terms of fault rate, with particular applications to large-scale databases and hypertext systems. Our prediction algorithms with particular applications to large-scale databases and hypertext systems. Our prediction algorithms for prefetching are novel in that they are based on data compression techniques that are both theoretically optimal and good in practice. Intuitively, in order to compress data effectively, you have to be able to predict future data well, and thus good data compressors should be able to predict well for purposes of prefetching. We show for powerful models such as Markov sources and mthe order Markov sources that the page fault rate incurred by our prefetching algorithms are optimal in the limit for almost all sequences of page requests.
156 citations