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arXiv 1402.3247cs.ITmath.IT
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基于学习的小基站缓存内容优化

Learning-Based Optimization of Cache Content in a Small Cell Base Station

Pol Blasco, Deniz Gunduz

摘要:

研究了回程容量有限的无线小小区基站(sBS)中缓存内容的最优放置问题。sBS具有大的高速缓冲存储器,并且通过将高数据率内容递送到其覆盖区域中的用户来提供内容级选择性卸载。sBS内容控制器(CC)的目标是将最流行的内容存储在sBS高速缓冲存储器中,使得可以直接从sBS获取最大量的数据,而不依赖于峰值业务时段期间的有限回程资源。如果预先知道受欢迎程度,则问题简化为背包问题。然而,在这项工作中,假设CC不知道文件的流行度简档,并且它只能观察对缓存内容的瞬时需求。因此,基于需求历史来优化缓存内容放置。通过以规则的时间间隔刷新高速缓存内容,CC尝试学习流行度简档,同时以可能的最佳方式利用有限的高速缓存容量。三种算法研究这个缓存内容放置问题,导致不同的开发探索权衡。我们提供了大量的数值模拟,以研究这些算法的时间演化,以及系统参数的影响,如文件的数量,用户的数量,缓存的大小,和偏度的流行配置文件,对性能的影响。结果表明,所提出的算法快速学习的流行配置文件的系统参数范围很广。

英文摘要:

Optimal cache content placement in a wireless small cell base station (sBS) with limited backhaul capacity is studied. The sBS has a large cache memory and provides content-level selective offloading by delivering high data rate contents to users in its coverage area. The goal of the sBS content controller (CC) is to store the most popular contents in the sBS cache memory such that the maximum amount of data can be fetched directly form the sBS, not relying on the limited backhaul resources during peak traffic periods. If the popularity profile is known in advance, the problem reduces to a knapsack problem. However, it is assumed in this work that, the popularity profile of the files is not known by the CC, and it can only observe the instantaneous demand for the cached content. Hence, the cache content placement is optimised based on the demand history. By refreshing the cache content at regular time intervals, the CC tries to learn the popularity profile, while exploiting the limited cache capacity in the best way possible. Three algorithms are studied for this cache content placement problem, leading to different exploitation-exploration trade-offs. We provide extensive numerical simulations in order to study the time-evolution of these algorithms, and the impact of the system parameters, such as the number of files, the number of users, the cache size, and the skewness of the popularity profile, on the performance. It is shown that the proposed algorithms quickly learn the popularity profile for a wide range of system parameters.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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