Collaborative Content Placement Among Wireless Edge Caching Stations With Time-to-Live Cache
Collaborative Content Placement Among Wireless Edge Caching Stations With Time-to-Live Cache
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DOI：
10.1109/tmm.2019.2929004
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发表时间：
2020-02
影响因子：
7.3
通讯作者：
Lixing Chen;Linqi Song;Jacob Chakareski;Jie Xu
中科院分区：
文献类型：
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作者：
Lixing Chen;Linqi Song;Jacob Chakareski;Jie Xu
使用无线边缘缓存站 (ECS) 网络在互联网边缘进行内容缓存最近被认为是减轻回程流量负担和提高 5G 网络体验质量的关键解决方案。本文研究的无线边缘缓存系统具有以下特点：首先，内容文件可以被分割成许多编码数据包，然后可以缓存在多个ECS中以进行协作内容交付；其次，服务提供商（SP）在ECS上部署生存时间缓存，每个缓存的内容文件都有一个需要保证的占用时间。第三，当用户随着时间的推移请求新内容时，要缓存的内容遵循随机过程到达缓存系统。与现有的确定要缓存哪些内容的工作不同，本文重点关注如何在ECS网络之间分发要缓存的内容的编码包，以减少内容下载时间。基于李亚普诺夫技术提出了一种新颖的内容放置策略，称为随机协作内容放置。该算法仅使用当前可用的信息做出内容放置决策，而无需预见未来的内容到达，利用编码缓存的空间内容流行度变化，并实现可证明的接近最佳的长期缓存性能。对现实世界的 YouTube 视频请求跟踪进行了模拟，结果表明，与各种基准方案相比，缓存性能有了巨大的改进。
Content caching at the Internet edge using a network of wireless edge caching stations (ECSs) is recently considered as a key solution to alleviating the backhaul traffic burden and improving the quality of experience in 5G networks. This paper studies wireless edge caching systems with the following features: first, content files can be partitioned into many coded packets, which then can be cached in multiple ECSs for collaborative content delivery; second, the service provider (SP) deploys time-to-live cache at ECSs and each cached content file has an occupancy time that needs to be guaranteed; third, the content-to-be-cached arrives at the caching system following a stochastic process as users request new content over time. Unlike existing works that determine which content to cache, this paper focuses on how to distribute the coded packets of content-to-be-cached among the network of ECSs in order to reduce the content downloading time. A novel content placement strategy, called stochastic collaborative content placement is proposed based on Lyapunov techniques. The proposed algorithm makes content placement decisions using only currently available information without foreseeing future content arrivals, takes advantage of the spatial content popularity variation with coded caching, and achieves the provable close-to-optimal long-term caching performance. Simulations are carried out on a real-world YouTube video request trace and the results demonstrate a tremendous caching performance improvement against a variety of benchmark schemes.