A novel cache size optimization scheme based on manifold learning in Content Centric Networking
A novel cache size optimization scheme based on manifold learning in Content Centric Networking
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DOI：
10.1016/j.jnca.2013.03.002
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发表时间：
2014
期刊：
影响因子：
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通讯作者：
Yuemei Xu;Yang Li;Tao Lin;Zihou Wang;Wenjia Niu;Hui Tang;S. Ci
中科院分区：
文献类型：
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作者：
Yuemei Xu;Yang Li;Tao Lin;Zihou Wang;Wenjia Niu;Hui Tang;S. Ci
内容中心网络(CCN)是一种新兴的网络体系结构，从端到端的连接模式转变为以内容为中心的通信模式。CCN中的每个路由器都有一个内容存储模块来缓存经过的块，并以任意的网络拓扑结构排列。为每台路由器分配适当的缓存大小对于提高网络性能和减少经济投资是非常重要的。以前的工作已经提出了几种不同的缓存分配方案，但这些方案带来的收益并不明显。本文将数据挖掘方法引入到缓存大小分配中。该算法使用流形学习来分析网络流量和用户行为的规律性，并根据路由器在内容传递中的角色对路由器进行分类。在流形学习嵌入结果的指导下，提出了一种新的缓存大小优化方案。已经进行了大量的实验来评估所提出的方案。仿真结果表明，该方案的性能优于现有的CCN缓存分配方案。
Content Centric Networking (CCN) is an emerging network architecture, shifting from anend-to-endconnection to acontent centriccommunication model. Each router in CCN has a content store module to cache the chunks passed by, and is arranged in an arbitrary network topology. It is important to allocate an appropriate cache size to each router in order to both improve the network performance and reduce the economic investment. Previous works have proposed several heterogeneous cache allocation schemes, but the gain brought by these schemes is not obvious. In this paper, we introduce a data mining method into the cache size allocation. The proposed algorithm uses manifold learning to analyze the regularity of network traffic and user behaviors, and classify routers based on their roles in the content delivery. Guided by the manifold learning embedding results, a novel cache size optimization scheme is developed. Extensive experiments have been performed to evaluate the proposed scheme. Simulation results show that the proposed scheme outperforms the existing cache allocation schemes in CCN.