Arthur Nádas
IEEE Transactions on Neural Networks
The topic of this paper is a probabilistic analysis of demand paging algorithms for storage hierarchies. Two aspects of algorithm performance are studied under the assumption that the sequence of page requests is statistically independent: the page fault probability for a fixed memory size and the variation of performance with memory. Performance bounds are obtained which are independent of the page request probabilities. It is shown that simple algorithms exist which yield fault probabilities close to optimal with only a modest increase in memory. © 1974, ACM. All rights reserved.
Arthur Nádas
IEEE Transactions on Neural Networks
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