Amortized efficiency of list update and paging rules
Communications of the ACM
A locally adaptive data compression scheme
Communications of the ACM
Journal of Algorithms
Elements of information theory
Elements of information theory
Practical prefetching via data compression
SIGMOD '93 Proceedings of the 1993 ACM SIGMOD international conference on Management of data
Randomized algorithms
Optimal prefetching via data compression
Journal of the ACM (JACM)
Computer architecture (2nd ed.): a quantitative approach
Computer architecture (2nd ed.): a quantitative approach
Online computation and competitive analysis
Online computation and competitive analysis
Optimal Prediction for Prefetching in the Worst Case
SIAM Journal on Computing
Paging against a distribution and IP networking
Journal of Computer and System Sciences
Some Distribution-Free Aspects of Paging Algorithm Performance
Journal of the ACM (JACM)
Can entropy characterize performance of online algorithms?
SODA '01 Proceedings of the twelfth annual ACM-SIAM symposium on Discrete algorithms
Information Theory and Reliable Communication
Information Theory and Reliable Communication
SIAM Journal on Computing
On-line Decision Making for a Class of Loss Functions via Lempel-Ziv Parsing
DCC '00 Proceedings of the Conference on Data Compression
Communications of the ACM - Interaction design and children
IEEE Transactions on Information Theory
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We focus in this work on an aspect of online computation that is not addressed by standard competitive analysis, namely, identifying request sequences for which nontrivial online algorithms are useful versus request sequences for which all algorithms perform equally poorly. The motivations for this work are advanced system and architecture designs which allow the operating system to dynamically allocate resources to online protocols such as prefetching and caching. To utilize these features, the operating system needs to identify data streams that can benefit from more resources. Our approach in this work is based on the relation between entropy, compression, and gambling, extensively studied in information theory. It has been shown that in some settings, entropy can either fully or at least partially characterize the expected outcome of an iterative gambling game. Our goal is to study the extent to which the entropy of the input characterizes the expected performance of online algorithms for problems that arise in computer applications. We study bounds based on entropy for three classical online problems---list accessing, prefetching, and caching. Our bounds relate the performance of the best online algorithm to the entropy, a parameter intrinsic to characteristics of the request sequence. This is in contrast to the competitive ratio parameter of competitive analysis, which quantifies the performance of the online algorithm with respect to an optimal offline algorithm. For the prefetching problem, we give explicit upper and lower bounds for the performance of the best prefetching algorithm in terms of the entropy of the request sequence. In contrast, we show that the entropy of the request sequence alone does not fully capture the performance of online list accessing and caching algorithms.