Optimal prefetching via data compression (extended abstract)
SFCS '91 Proceedings of the 32nd annual symposium on Foundations of computer science
IEEE Transactions on Information Theory
On sequential strategies for loss functions with memory
IEEE Transactions on Information Theory
Entropy-based bounds for online algorithms
ACM Transactions on Algorithms (TALG)
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Prefetching in computer memory architectures is formalized as a sequential decision problem in which the instantaneous losses depend not only on the current action-observation pair, as in the traditional formulation, but also on past pairs. Motivated by the prefetching application, we study a class of loss functions that admit an efficient on-line decision algorithm. The algorithm uses the LZ78 parsing rule to dynamically build a tree, different from the classical LZ78 tree, and makes decisions based on the current node in a traversal path, determined by the sequence of observations. The asymptotic performance is essentially as good as that of the best finite-state strategy determined in hindsight, with full knowledge of the given sequence of observations. The related notion of delayed FS predictability is introduced, and its properties are studied.