A Universal Online Caching Algorithm Based on Pattern Matching
We present a universal algorithm for the classical online problem of caching or demand paging. We consider the caching problem when the page request sequence is drawn from an unknown probability distribution and the goal is to devise an efficient algorithm whose performance is close to the optimal online algorithm which has full knowledge of the underlying distribution. Most previous works have devised such algorithms for specific classes of distributions with the assumption that the algorithm has full knowledge of the source. In this paper, we present a universal and simple algorithm based on pattern matching for mixing sources (includes Markov sources). The expected performance of our algorithm is within 4+o(1) times the optimal online algorithm (which has full knowledge of the input model and can use unbounded resources).
KeywordsOnline computation Caching Universal algorithm Stochastic model
Unable to display preview. Download preview PDF.
- 4.Curewitz, K., Krishnan, P., Vitter, J.S.: Practical prefetching via data compression. In: Proceedings of the ACM SIGMOD International Conference on Management of Data, pp. 257–266 (1993) Google Scholar
- 10.Hannan, J.F.: Approximation to Bayes risk in repeated plays. In: Contributions to the Theory of Games. Annals of Mathematics Studies, vol. 3, pp. 97–139. Princeton Univ. Press, Princeton (1957) Google Scholar
- 18.Pandurangan, G., Upfal, E.: Entropy-based bounds for online algorithms. ACM Trans. Algorithms 3(1) (2007) Google Scholar
- 22.Weinberger, M., Ordentlich, E.: On-line decision making for a class of loss functions via Lempel-Ziv parsing. In: Proc. of the IEEE Data Compression Conference, pp. 163–172 (2000) Google Scholar