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Sharing Cache Resources Among Content Providers: A Utility-Based Approach

机译:在内容提供商之间共享缓存资源:一种基于实用程序的方法

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In this paper, we consider the problem of allocating cache resources among multiple content providers. The cache can be partitioned into slices and each partition can be dedicated to a particular content provider or shared among a number of them. It is assumed that each partition employs the least recently used policy for managing content. We propose utility-driven partitioning, where we associate with each content provide a utility that is a function of the hit rate observed by the content provider. We consider two scenarios: 1) content providers serve disjoint sets of files and 2) there is some overlap in the content served by multiple content providers. In the first case, we prove that cache partitioning outperforms cache sharing as cache size and a number of contents served by providers go to infinity. In the second case, it can be beneficial to have separate partitions for overlapped content. In the case of two providers, it is usually always beneficial to allocate a cache partition to serve all overlapped content and separate partitions to serve the non-overlapped contents of both providers. We establish conditions when this is true asymptotically but also present an example where it is not true asymptotically. We develop online algorithms that dynamically adjust partition sizes in order to maximize the overall utility and prove that they converge to optimal solutions, and through numerical evaluations we show they are effective.
机译:在本文中,我们考虑了在多个内容提供者之间分配缓存资源的问题。可以将缓存划分为片,每个分区可以专用于特定的内容提供者,也可以在其中的多个共享者之间共享。假定每个分区都采用最近最少使用的策略来管理内容。我们建议使用实用程序驱动的分区,在该分区中,我们与每个内容相关联,以提供一种实用程序,该实用程序是内容提供商所观察到的命中率的函数。我们考虑两种情况:1)内容提供者提供不连续的文件集; 2)多个内容提供者提供的内容中存在一些重叠。在第一种情况下,我们证明了高速缓存分区的性能优于高速缓存共享,因为高速缓存的大小和提供程序所服务的许多内容达到无穷大。在第二种情况下,对重叠的内容使用单独的分区可能是有益的。对于两个提供程序,通常分配一个缓存分区以服务所有重叠的内容,分配单独的分区来服务两个提供程序的非重叠内容通常总是有益的。我们建立渐近为真时的条件,但也给出一个渐近为非真值的示例。我们开发了可动态调整分区大小的在线算法,以最大程度地发挥整体效用,并证明它们可以收敛到最佳解决方案,并且通过数值评估,我们证明它们是有效的。

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