Predict-then-Prefetch Caching Strategy to Enhance QoE in 5G Networks

Meng Sun, Hao-peng Chen, Buqing Shu
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引用次数: 4

Abstract

With the unprecedented traffic demand from various mobile devices, bad quality of experience arises in traditional reactive networks, such as long loading time and frozen in the middle. This paper presents Predict-then-Prefetch caching strategy in 5G networks to improve the quality of experience. This strategy partitions the capacity of the base stations into the proactive cache to prefetch popular content for a sum total maximum of popularity and the reactive one to cache content which is unpopular or whose popularity can’t be forecast inaccurately. It is demonstrated that Predict-then-Prefetch caching strategy has the best proportion of the proactive cache with different percentages of time-related content. Under this best proportion of the circumstances where all content is time-related, this strategy improves hit ratio by 30% and reduces latency by 50% in the architecture of 200M small base stations, which could enhance the quality of experience to a great degree.
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预测然后预取缓存策略提升 5G 网络的 QoE
随着各种移动设备带来前所未有的流量需求,传统的反应式网络出现了加载时间长、中间冻结等不良体验。本文提出了 5G 网络中的 "先预测后预取"(Predict-then-Prefetch)缓存策略,以改善体验质量。该策略将基站容量划分为主动缓存和被动缓存,前者用于预取热门内容,以获得热门程度的总和最大值,后者用于缓存不热门或热门程度无法准确预测的内容。结果表明,"预测-然后-预取 "缓存策略在不同比例的时间相关内容中具有最佳的主动缓存比例。在这种所有内容都与时间相关的最佳比例情况下,该策略在 200M 小型基站架构中提高了 30% 的命中率,减少了 50% 的延迟,可以在很大程度上提高体验质量。
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