论文标题
缓存更新策略最小化信息的年龄与时变文件的受欢迎程度
Cache Updating Strategy Minimizing the Age of Information with Time-Varying Files' Popularities
论文作者
论文摘要
我们考虑更新本地缓存策略，该缓存通过带宽受限的链接从远程服务器下载时间敏感的文件。根据Markov链结构随时间变化，本地用户从缓存中随机请求文件。我们通过信息年龄（AOI）来衡量所需的时间敏感文件的新鲜度。然后，目标是通过适当设计本地缓存的下载策略来最大程度地减少所有请求文件的平均AOI。为了实现这一目标，原始问题是放松的，并将其施加在约束的马尔可夫决策问题（CMDP）中，我们使用拉格朗日方法和线性编程来解决该问题。受此解决方案的启发，我们提出了一种实用的缓存更新策略，该策略符合原始问题的所有约束。在某些假设下，实际更新策略被证明是大量文件的渐近制度中原始问题的最佳选择。 对于有限数量的文件，我们通过数值模拟显示了与传统的Square-rot-law策略（对于固定的非时变文件受欢迎程度最佳）相比，我们的实用更新策略的增益。
We consider updating strategies for a local cache which downloads time-sensitive files from a remote server through a bandwidth-constrained link. The files are requested randomly from the cache by local users according to a popularity distribution which varies over time according to a Markov chain structure. We measure the freshness of the requested time-sensitive files through their Age of Information (AoI). The goal is then to minimize the average AoI of all requested files by appropriately designing the local cache's downloading strategy. To achieve this goal, the original problem is relaxed and cast into a Constrained Markov Decision Problem (CMDP), which we solve using a Lagrangian approach and Linear Programming. Inspired by this solution for the relaxed problem, we propose a practical cache updating strategy that meets all the constraints of the original problem. Under certain assumptions, the practical updating strategy is shown to be optimal for the original problem in the asymptotic regime of a large number of files. For a finite number of files, we show the gain of our practical updating strategy over the traditional square-root-law strategy (which is optimal for fixed non time-varying file popularities) through numerical simulations.
