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A Universal Online Caching Algorithm Based on Pattern Matching

机译:一种基于模式匹配的通用在线缓存算法

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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). Keywords Online computation - Caching - Universal algorithm - Stochastic model A preliminary version of this paper was presented at the IEEE International Symposium on Information Theory, Adelaide, Australia, 2005.
机译:我们提出了一种用于缓存或需求分页的经典在线问题的通用算法。当从未知的概率分布中提取页面请求序列时,我们考虑了缓存问题,目的是设计一种性能接近于对底层分布有充分了解的最佳在线算法的高效算法。以前的大多数工作都在特定算法下对此类特定分布进行了设计,但前提是该算法对源完全了解。在本文中,我们提出了一种基于模式匹配的通用且简单的混合源算法(包括马尔可夫源)。我们的算法的预期性能在最佳在线算法(具有完整的输入模型知识并可以使用无穷资源)的4 + o(1)倍之内。关键字在线计算-缓存-通用算法-随机模型本文的初步版本在2005年澳大利亚阿德莱德举行的IEEE国际信息理论研讨会上发表。

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