Ins and Outs: Optimal Caching and Re-Caching Policies in Mobile Networks

Wei Bao, Dong Yuan, Keqi Shi, Weiyu Ju, Albert Y. Zomaya
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引用次数: 5

Abstract

Caching is essential for data-intensive mobile applications to reduce duplicated data transmission. In this paper, we study the optimal probabilistic caching and re-caching policies in mobile networks as the file popularity may change over time. We propose a Probabilistic File Re-caching (PFR) policy to match the updated popularity. Through PFR, files cached (resp. not cached) are probabilistically opted out (resp. opted in). PFR is with substantial advantages: (1) PFR can handle a huge combinatorial number of all possible situations. (2) The expected number of replaced files is minimized. (3) The computational complexity of PFR is low. Second, we study a utility optimization problem in the mobile network, in order to optimally decide the probability that each file is cached and whether a file should be downloaded from a peer device or directly from the server. Even though the optimization problem is non-convex programming in nature, we devise a computationally efficient Optimal Probabilistic Caching and Requesting (OPCR) policy, through decoupling the decision variables, to derive a globally optimal solution. Finally, we develop a real-world prototype and conduct trace-driven simulations to validate and evaluate our proposed PFR and OPCR policies.
来龙去脉:移动网络中的最佳缓存和再缓存策略
缓存对于数据密集型移动应用程序来说是必不可少的,可以减少重复的数据传输。本文研究了移动网络中文件受欢迎程度随时间变化的最优概率缓存和再缓存策略。我们提出了一种概率文件重缓存(PFR)策略来匹配更新的流行度。通过PFR,缓存的文件。未缓存)很可能被选择退出(参见。选择)。PFR具有显著的优势:(1)PFR可以处理所有可能情况的大量组合。(2)期望被替换文件的数量最小化。(3) PFR的计算复杂度较低。其次,我们研究了移动网络中的效用优化问题,以最优地确定每个文件被缓存的概率,以及文件是应该从对等设备下载还是直接从服务器下载。尽管优化问题本质上是非凸规划,但我们设计了一种计算效率高的最优概率缓存和请求(OPCR)策略,通过解耦决策变量,推导出全局最优解。最后,我们开发了一个真实世界的原型,并进行了跟踪驱动的模拟,以验证和评估我们提出的PFR和OPCR策略。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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