Amortized efficiency of list update and paging rules
Communications of the ACM
Journal of Algorithms
Competitive paging with locality of reference
Selected papers of the 23rd annual ACM symposium on Theory of computing
Randomized and multipointer paging with locality of reference
STOC '95 Proceedings of the twenty-seventh annual ACM symposium on Theory of computing
Strongly Competitive Algorithms for Paging with Locality of Reference
SIAM Journal on 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
Experimental studies of access graph based heuristics: beating the LRU standard?
SODA '97 Proceedings of the eighth annual ACM-SIAM symposium on Discrete algorithms
Competitive analysis of randomized paging algorithms
Theoretical Computer Science
The working set model for program behavior
Communications of the ACM
SIAM Journal on Computing
Flexible reference trace reduction for VM simulations
ACM Transactions on Modeling and Computer Simulation (TOMACS)
The EELRU adaptive replacement algorithm
Performance Evaluation
On paging with locality of reference
Journal of Computer and System Sciences
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
IEEE Transactions on Software Engineering
Paging and list update under bijective analysis
SODA '09 Proceedings of the twentieth Annual ACM-SIAM Symposium on Discrete Algorithms
Modern Operating Systems: Jumpstart Sampling Edition
Modern Operating Systems: Jumpstart Sampling Edition
A study of replacement algorithms for a virtual-storage computer
IBM Systems Journal
Algorithmica - Special issue: Algorithms, Combinatorics, & Geometry
Parameterized analysis of paging and list update algorithms
WAOA'09 Proceedings of the 7th international conference on Approximation and Online Algorithms
ONLINEMIN: a fast strongly competitive randomized paging algorithm
WAOA'11 Proceedings of the 9th international conference on Approximation and Online Algorithms
Engineering efficient paging algorithms
SEA'12 Proceedings of the 11th international conference on Experimental Algorithms
Improved space bounds for strongly competitive randomized paging algorithms
ICALP'13 Proceedings of the 40th international conference on Automata, Languages, and Programming - Volume Part I
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Competitive analysis was often criticized because of its too pessimistic guarantees which do not reflect the behavior of paging algorithms in practice. For instance, many deterministic paging algorithms achieve the optimal competitive ratio of k, yet LRU and its variants clearly outperform the rest in practice. In this paper we aim to reuse and refine insights from the competitive analysis to obtain new algorithms that cause few cache misses in practice. We propose a new measure of the "evilness" of the adversary, which results in a parametrization of the input that we denote attack rate. This measure is based on the characterization in [22] of the optimal offline algorithm and uses the fact that a number of pages are for sure in its memory. We show that the attack rate r is a tight bound on the competitive ratio of deterministic paging algorithms and give experimental results which show that r is usually much smaller than the cache size k and thus provides more realistic upper bounds for the competitive ratio of existing algorithms. Furthermore, we show that our input parametrization compares favorably concerning the fault rate with approaches based on locality of reference by Albers et al. [2] and Dorrigiv et al. [14] We use a priority-based framework, which always yields r-competitive algorithms regardless of the priority assignment. In this framework, LRU can be obtained under a certain priority assignment and is thus only one algorithm among many other r-competitive ones. Using the enhanced flexibility given by this framework, we give a priority policy which leads to an algorithm outperforming LRU, RLRU and other practical algorithms on a wide selection of real-world cache traces.