Coded caching utilizes proper file subpacketization and coded delivery to make full use of the multicast opportunities in content delivery, to alleviate file transfer load in massive content delivery scenarios. Most existing work considers deterministic environments. An important practical topic is to characterize the impact of the uncertainty from user inactivity on coded caching. We consider a one server cache-enabled network under homogeneous file and network settings in presence of user inactivity. Unlike random or probabilistic caching studied in the literature, deterministic coded caching is considered, with the objective to minimize the worst-case backhaul load by optimizing the file subpacketization and the caching strategy. First, a coded caching method is used, where each file is split into the same type of fragments labeled using sets with fixed cardinality, and the optimality of the selected cardinality is proved. Optimal file subpacketization by splitting the file into multiple types of fragments labeled with multiple cardinalities is then discussed. We show that the closed-form optimum turns out to be given by a fixed cardinality -- optimizing for user inactivity only affects file delivery, cache placement is not affected. A decentralized version is also discussed and analyzed, where each user fills its storage independently at random without centralized coordination, and user inactivity is taken into account in file delivery. Simulation results show that the optimization based centralized coded caching scheme provides performance comparable to the ideal scenario assuming full knowledge of user inactivity in the placement phase, while decentralized caching performs slightly worse against user inactivity.
翻译:代码化的缓存利用了适当的文件子包装和编码交付,以充分利用内容交付中的多端机会,减轻大量内容交付情况下的文件传输负荷。 大多数现有工作都考虑到确定性环境。 一个重要的实际议题是描述用户不活动产生的不确定性对编码焦刻作用的影响。 我们考虑在用户不活动的情况下,在同质文件和网络设置下使用一个服务器缓存功能驱动的网络。 与文献中研究的随机或概率性缓存不同, 考虑确定性编码缓存, 目的是通过优化文件的子包装和缓冲战略, 最大限度地减少最坏情况的背部负负负负。 首先, 使用编码化的缓存方法, 将每个文件拆分成使用固定底部的标签, 并证明所选的“ 最优化” 。 然后讨论通过将文件分解文档分为多种类型, 将文件标有多重基本底部的碎片分解性缓存, 显示封闭式最优化由固定的基底部负重负负负负负负负负负, 用户最优化的递缩性在不全级的交付阶段进行优化,, 将用户的递归正化的递归为在分析中,, 的中央化的递缩化的递解式的递归为对用户的递解的递解的递解的递解的递解的递归为对用户的递归正的递归的递归为对的递制, 。