命名数据网中基于非均匀EWMA-PCA的缓存容量分配方案
Non-uniform EWMA-PCA based cache size allocation scheme in Named Data Networks
摘要：
作为一种以数据为中心的支持缓存的体系结构，命名数据网络（NDN）被认为是当前以主机为中心的基于IP的Internet基础设施的适当替代方案。利用网络内缓存、基于名称的路由和接收方驱动的会话，NDN可以极大地提高互联网资源的使用方式。NDN中的一个关键问题是缓存分配和管理过程。我们在这项研究中的主要贡献是内存需求的分析，以分配合适的内容存储大小的NDN路由器，相对于长期的基于中心的度量和指数加权移动平均（EWMA）的短期参数，如用户的行为和传出流量的综合影响。为了确定在这样大的数据集的相关性，数据采掘方法可以证明是有价值的研究人员。在本文中，我们采用数据融合的方法，即主成分分析（PCA），发现从短期和长期的路由器参数的关系。PCA的输出，挖掘出原始数据集，用于分配适当的缓存大小的路由器。评估结果表明，在源，和NDN路由器的内容存储的命中率增加。此外，对于建议的高速缓存大小分配方案，在NDN路由器的未满足和未决的利益的数量小于度中心的高速缓存大小计划。
英文摘要：
As a data-centric cache-enabled architecture, Named Data Networking (NDN) is considered to be an appropriate alternative to the current host-centric IP-based Internet infrastructure. Leveraging in-network caching, name-based routing, and receiver-driven sessions, NDN can greatly enhance the way Internet resources are being used. A critical issue in NDN is the procedure of cache allocation and management. Our main contribution in this research is the analysis of memory requirements to allocate suitable Content-Store size to NDN routers, with respect to combined impacts of long-term centrality-based metric and Exponential Weighted Moving Average (EWMA) of short-term parameters such as users behaviors and outgoing traffic. To determine correlations in such large data sets, data mining methods can prove valuable to researchers. In this paper, we apply a data-fusion approach, namely Principal Component Analysis (PCA), to discover relations from short- and long-term parameters of the router. The output of PCA, exploited to mine out raw data sets, is used to allocate a proper cache size to the router. Evaluation results show an increase in the hit ratio of Content-Stores in sources, and NDN routers. Moreover, for the proposed cache size allocation scheme, the number of unsatisfied and pending Interests in NDN routers is smaller than the Degree-Centrality cache size scheme.
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。
