Caching content is an inherent feature of Named Data Networks. Limited cache capacity of routers warrants that the choice of content being cached is judiciously done. Existing techniques resort to caching popular content to maximize utilization. However, these methods experience significant overhead for coordinating and estimating the popularity of content. To address this issue, in this paper, we present CPePC, which is a cooperative caching technique designed to improve performance. It accomplishes this through a combination of two factors. First, CPePC enhances efficiency by minimizing the overhead of popularity estimation. Second, it forecasts a parameter that governs caching decisions. Efficiency in popularity estimation is achieved by dividing the network into several non-overlapping communities using a community estimation algorithm and selecting a leader node to coordinate this on behalf of all the nodes in the community. CPePC bases its caching decisions by predicting a parameter whose value is estimated using current cache occupancy and the popularity of the content into account. We present algorithms for community detection, leader selection, content popularity estimation, and caching decisions made by the CPePC method. We evaluate and compare it with six other state-of-the-art caching techniques, with simulations performed using a discrete event simulator to show that it outperforms others.

翻译：内容缓存是命名数据网络的固有特性。路由器有限的缓存容量要求对缓存内容的选择必须审慎进行。现有技术通常采用缓存流行内容以最大化利用率。然而，这些方法在协调与估计内容流行度时会产生显著开销。为解决这一问题，本文提出CPePC——一种旨在提升性能的协作式缓存技术。其优势主要通过两方面实现：首先，CPePC通过最小化流行度估计开销来提升效率；其次，该方法能预测控制缓存决策的关键参数。流行度估计的效率提升是通过社区发现算法将网络划分为若干非重叠社区，并选取领导节点代表社区内所有节点进行协调而实现的。CPePC的缓存决策基于对特定参数的预测，该参数值通过综合考虑当前缓存占用率与内容流行度进行估算。本文提出了适用于CPePC方法的社区检测、领导节点选择、内容流行度估计及缓存决策算法。通过离散事件仿真器进行模拟实验，我们将本方法与六种先进缓存技术进行对比评估，结果表明CPePC性能优于其他方案。