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A utility optimization approach to network cache design

机译:网络缓存设计的实用程序优化方法

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In any caching system, the admission and eviction policies determine which contents are added and removed from a cache when a miss occurs. Usually, these policies are devised so as to mitigate staleness and increase the hit probability. Nonetheless, the utility of having a high hit probability can vary across contents. This occurs, for instance, when service level agreements must be met, or if certain contents are more difficult to obtain than others. In this paper, we propose utility-driven caching, where we associate with each content a utility, which is a function of the corresponding content hit probability. We formulate optimization problems where the objectives are to maximize the sum of utilities over all contents. These problems differ according to the stringency of the cache capacity constraint. Our framework enables us to reverse engineer classical replacement policies such as LRU and FIFO, by computing the utility functions that they maximize. We also develop online algorithms that can be used by service providers to implement various caching policies based on arbitrary utility functions.
机译:在任何缓存系统中,准入和逐出策略都会确定在发生未命中时从缓存中添加和删除哪些内容。通常,设计这些策略是为了减轻陈旧性并增加命中率。但是,具有高命中率的实用程序可能会因内容而异。例如,当必须满足服务水平协议时,或者某些内容比其他内容更难获得时,就会发生这种情况。在本文中,我们提出了实用程序驱动的缓存,其中我们将每个实用程序与一个实用程序相关联,该实用程序是相应内容命中概率的函数。我们制定优化问题,目标是在所有内容上最大化实用程序的总和。这些问题根据高速缓存容量约束的严格性而有所不同。我们的框架使我们能够通过计算最大化的效用函数来逆向工程经典的替换策略,例如LRU和FIFO。我们还开发了在线算法,服务提供商可以使用该算法来基于任意实用程序功能实施各种缓存策略。

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