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Competitive paging algorithms

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TLDR
The marking 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 of 2Hk of optimum.
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This article is published in Journal of Algorithms.The article was published on 1991-12-01 and is currently open access. It has received 489 citations till now. The article focuses on the topics: Page replacement algorithm & K-server problem.

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Citations
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Journal ArticleDOI

SIGACT news online algorithms column 2

TL;DR: This issue’s column contains an article by Wolf Bein and Larry Larmore with a gentle introduction to the concepts of trackless and limited-bookmark online algorithms for paging and for the k-server problem.
Posted Content

Online $k$-Taxi via Double Coverage and Time-Reverse Primal-Dual

TL;DR: The classic Double Coverage algorithm has competitive ratio $2^k-1$ on HSTs, matching a recent lower bound for deterministic algorithms, and a dual fitting proof of the $k$-competitiveness of Double Coverage for the k-server problem on trees is provided.
Proceedings ArticleDOI

Paging and the Address-Translation Problem

TL;DR: The Address-Translation Problem (ATP) as discussed by the authors addresses the problem of maintaining a TLB, a page table, and RAM in order to minimize the total cost of both TLB misses and IOs.

A Platform for Scalable Low-Latency Analytics using MapReduce

Boduo Li
TL;DR: A PLATFORM for SCalable Low-Latency Analysis using MAPREDUCE for low-latency analysis of fluid dynamics and its applications in the industrial setting is presented.
References
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Journal ArticleDOI

Amortized efficiency of list update and paging rules

TL;DR: This article shows that move-to-front is within a constant factor of optimum among a wide class of list maintenance rules, and analyzes the amortized complexity of LRU, showing that its efficiency differs from that of the off-line paging rule by a factor that depends on the size of fast memory.
Proceedings ArticleDOI

Probabilistic computations: Toward a unified measure of complexity

TL;DR: Two approaches to the study of expected running time of algoritruns lead naturally to two different definitions of intrinsic complexity of a problem, which are the distributional complexity and the randomized complexity, respectively.
Journal ArticleDOI

Competitive snoopy caching

TL;DR: This work presents new on-line algorithms to be used by the caches of snoopy cache multiprocessor systems to decide which blocks to retain and which to drop in order to minimize communication over the bus.
Journal ArticleDOI

Competitive algorithms for server problems

TL;DR: This paper seeks to develop on-line algorithms whose performance on any sequence of requests is as close as possible to the performance of the optimum off-line algorithm.
Proceedings ArticleDOI

Competitive algorithms for on-line problems

TL;DR: This paper presents several general results concerning competitive algorithms, as well as results on specific on-line problems.
Related Papers (5)
Frequently Asked Questions (11)
Q1. What are the contributions mentioned in the paper "Competitive paging algorithms" ?

In this paper, the authors proposed a method for the analysis of the relationship between computer science degrees and their application in the field of artificial intelligence. 

Karlin et al. [8] have shown that for two servers in a graph that is an isosceles triangle the best competitive factor that can be achieved is a constant that approaches e/(e - 1) z 1.582 as the length of the similar sides go to infinity. 

A randomized on-line algorithm may be viewed as basing its actions on the request sequence (T presented to it and on an infinite sequence p of independent unbiased random bits. 

The marking algorithm is strongly competitive (its competitive factor is Hk) if k = n - 1, but it is not strongly competitive if k < n - 1. 

They showed that LRU running with k servers performs within a factor of k/(k - h + 1) of any off-line algorithm with h 5 k servers and that this is the minimum competitive factor that can be achieved. 

They showed that no deterministic algorithm for the k-server problem can be better than k-competitive, they gave k-competitive algorithms for the case when k = 2 and k = II - 1, and they conjectured that there exists a k-competitive k-server algorithm for any graph. 

The adversary is, however, able to maintain a vector p = (pl, p2,. . . , p,) of probabilities, where pi is the probability that vertex i is not covered by a server. 

In that proof, deterministic on-line algorithms B(l), B(2), . . . , B(m) of type (k, n) were given, and the deterministic on-line algorithm A of type (k, n) was constructed to be &)-competitive against B(i) for each i. 

If the total expected cost ends up exceeding l/u, then an arbitrary request is made to an unmarked vertex, and the subphase is over. 

During this phase exactly the vertices of S were requested, so since A is lazy, the authors know that at least d’ of A’s servers were outside of S during the entire phase. 

Armed with these tools (the marking and the probability vector), the adversary can generate a sequence such that the expected cost of each phase to A is H,,-l, and the cost to the optimum off-line algorithm is 1.