Access Graphs Results for LRU versus FIFO under Relative Worst Order Analysis

Boyar, Joan; Gupta, Sushmita; Larsen, Kim S.

doi:10.1007/978-3-642-31155-0_29

Cited by 14 publications

(10 citation statements)

References 25 publications

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“…Early work includes access graph results, initiated by Borodin et al [8]. A recent account for related work in continuation of the initial access graph results was given by Boyar, Gupta, and Larsen [10]. More distributional models have also been developed by Albers, Favrholdt, and Giel [1].…”

Section: Relaxing the Online Constraintmentioning

confidence: 97%

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Online multi-coloring with advice

Christ

Favrholdt

Larsen

2015

Theoretical Computer Science

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We consider the problem of online graph multi-coloring with advice. Multi-coloring is often used to model frequency allocation in cellular networks. We give several nearly tight upper and lower bounds for the most standard topologies of cellular networks, paths and hexagonal graphs. For the path, negative results trivially carry over to bipartite graphs, and our positive results are also valid for bipartite graphs. For hexagonal graphs, negative results trivially carry over to 3-colorable graphs, and most of our positive results do as well. The advice given represents information that is likely to be available, studying for instance the data from earlier similar periods of time.

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Section: Relaxing the Online Constraintmentioning

confidence: 97%

“…Then i requests to each of v 2 and v 3 . To give all sequences the same length, the sequence ends with n − 2m − 2i requests distributed as evenly as possible among v 6 , v 8 , and v 10 .…”

Section: Lower Boundsmentioning

confidence: 99%

Online multi-coloring with advice

Christ

Favrholdt

Larsen

2015

Theoretical Computer Science

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“…Using RWOA, [22] proved that on the primary building blocks of access graphs, paths and cycles, LRU is strictly better than FIFO.…”

Section: Access Graphs For Modeling Locality Of Reference For Pagingmentioning

confidence: 99%

Relative Worst-Order Analysis: A Survey

Boyar

Favrholdt

Larsen

2018

Adventures Between Lower Bounds and Higher Altitudes

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“…These include (a) the max/max ratio [6], (b) bijective and average analysis [2,3], (c) the relative worst-order ratio [7,8], (e) relative interval analysis [9,14] and (f) parametrized analysis [11]. In a bijective analysis two algorithms alg 1 and alg 2 are compared on permutations of the same requests.…”

Section: Introductionmentioning

confidence: 99%

Quantifying Competitiveness in Paging with Locality of Reference

Albers

Frascaria

2018

Algorithmica

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The classical paging problem is to maintain a two-level memory system so that a sequence of requests to memory pages can be served with a small number of faults. Standard competitive analysis gives overly pessimistic results as it ignores the fact that real-world input sequences exhibit locality of reference. Initiated by a paper of Borodin et al. (J Comput Syst Sci 50:244-258, 1995) there has been considerable research interest in paging with locality of reference. In this paper we study the paging problem using an intuitive and simple locality model that records inter-request distances in the input. A characteristic vector C defines a class of request sequences with certain properties on these distances. The concept was introduced by Panagiotou and Souza (In: Proceedings of 38th annual ACM symposium on theory of computing (STOC), 2006). As a main contribution we develop new and improved bounds on the performance of important paging algorithms. A strength and novelty of the results is that they express algorithm performance in terms of locality parameters. In a first step we develop a new lower bound on the number of page faults incurred by an optimal offline algorithm opt. The bound is tight up to a small additive constant. Technically, the result relies on a new approach of relating the number of page faults to the number of memory hits and amortizing suitably. Based on these expressions for opt's cost, we obtain nearly tight upper and lower bounds on lru's competitiveness, given any characteristic vector C. Furthermore, we compare lru to fifo and fwf. For the first time we show bounds that quantify the difference between lru's performance and that of the other two strategies. The results imply that lru is strictly superior on inputs with a high degree of locality of reference. There exist general input families for which lru achieves constant competitive ratios whereas the guarantees of fifo and fwf tend to k, the size of the fast memory. Finally, we report on an experimental study that demonstrates that our theoretical bounds are very close to the experimentally observed ones. Hence our contributions bring competitive paging again closer to practice.

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Access Graphs Results for LRU versus FIFO under Relative Worst Order Analysis

Cited by 14 publications

References 25 publications

Online multi-coloring with advice

Online multi-coloring with advice

Relative Worst-Order Analysis: A Survey

Quantifying Competitiveness in Paging with Locality of Reference

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