A randomized algorithm for two servers in cross polytope spaces
Theoretical Computer Science
Outperforming LRU via competitive analysis on parametrized inputs for paging
Proceedings of the twenty-third annual ACM-SIAM symposium on Discrete 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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We introduce the novel concept of knowledge states. The knowledge state approach can be used to construct competitive randomized online algorithms and study the trade-off between competitiveness and memory. Many well-known algorithms can be viewed as knowledge state algorithms. A knowledge state consists of a distribution of states for the algorithm, together with a work function which approximates the conditional obligations of the adversary. When a knowledge state algorithm receives a request, it then calculates one or more “subsequent” knowledge states, together with a probability of transition to each. The algorithm uses randomization to select one of those subsequents to be the new knowledge state. We apply this method to randomized k-paging. The optimal minimum competitiveness of any randomized online algorithm for the k-paging problem is the kth harmonic number, $H_{k}=\sum^{k}_{i=1}\frac{1}{i}$. Existing algorithms which achieve that optimal competitiveness must keep bookmarks, i.e., memory of the names of pages not in the cache. An H k -competitive randomized algorithm for that problem which uses O(k) bookmarks is presented, settling an open question by Borodin and El-Yaniv. In the special cases where k=2 and k=3, solutions are given using only one and two bookmarks, respectively.