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The Design of Dynamic Probabilistic Caching with Time-Varying Content Popularity
IEEE Transactions on Mobile Computing ( IF 7.7 ) Pub Date : 2020-01-01 , DOI: 10.1109/tmc.2020.2967038
Jie Gao , Shan Zhang , Lian Zhao , Xuemin Shen

In this paper, we design dynamic probabilistic caching for the scenario when the instantaneous content popularity may vary with time while it is possible to predict the average content popularity over a time window. Based on the average content popularity, optimal content caching probabilities can be found, e.g., from solving optimization problems, and existing results in the literature can implement the optimal caching probabilities via static content placement. The objective of this work is to design dynamic probabilistic caching that: i) converge (in distribution) to the optimal content caching probabilities under time-invariant content popularity, and ii) adapt to the time-varying instantaneous content popularity under time-varying content popularity. Achieving the above objective requires a novel design of dynamic content replacement because static caching cannot adapt to varying content popularity while classic dynamic replacement policies, such as LRU, cannot converge to target caching probabilities (as they do not exploit any content popularity information). We model the design of dynamic probabilistic replacement policy as the problem of finding the state transition probability matrix of a Markov chain and propose a method to generate and refine the transition probability matrix. Extensive numerical results are provided to validate the effectiveness of the proposed design.

中文翻译:

具有时变内容流行度的动态概率缓存设计

在本文中,我们为瞬时内容流行度可能随时间变化的场景设计了动态概率缓存,同时可以预测时间窗口内的平均内容流行度。基于平均内容流行度,可以找到最优内容缓存概率,例如,通过解决优化问题,并且文献中的现有结果可以通过静态内容放置来实现最优缓存概率。这项工作的目标是设计动态概率缓存:i) 收敛(分布)到时不变内容流行度下的最佳内容缓存概率,以及 ii) 适应时变内容下的时变瞬时内容流行度人气。实现上述目标需要一种新颖的动态内容替换设计,因为静态缓存不能适应变化的内容流行度,而经典的动态替换策略,如 LRU,不能收敛到目标缓存概率(因为它们不利用任何内容流行度信息)。我们将动态概率替换策略的设计建模为寻找马尔可夫链的状态转移概率矩阵的问题,并提出了一种生成和细化转移概率矩阵的方法。提供了广泛的数值结果来验证所提出的设计的有效性。无法收敛到目标缓存概率(因为它们不利用任何内容流行信息)。我们将动态概率替换策略的设计建模为寻找马尔可夫链的状态转移概率矩阵的问题,并提出了一种生成和细化转移概率矩阵的方法。提供了广泛的数值结果来验证所提出的设计的有效性。无法收敛到目标缓存概率(因为它们不利用任何内容流行信息)。我们将动态概率替换策略的设计建模为寻找马尔可夫链的状态转移概率矩阵的问题,并提出了一种生成和细化转移概率矩阵的方法。提供了广泛的数值结果来验证所提出的设计的有效性。
更新日期:2020-01-01
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