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arXiv 1802.03072cs.NI
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命名数据网络中模糊兴趣转发的实验研究

Experimentation With Fuzzy Interest Forwarding in Named Data Networking

Spyridon Mastorakis, Kevin Chan, Bongjun Ko, Alexander Afanasyev, Lixia Zhang

摘要:

在当前的命名数据网络实现中,转发数据请求需要找到请求中携带的名称前缀与转发表条目之间的精确匹配。然而,消费者可能并不总是知道他们所需数据的确切命名或确切前缀。当前解决这个问题的方法--建立命名约定并执行名称查找--在高度特设、异构和动态的环境中是不可行的:相同的数据可以使用不同的术语甚至语言命名,命名约定可能是最小的,如果它们存在的话,并且名称查找可能是昂贵的。在本文中,我们提出了一个模糊的兴趣转发方法,利用语义相似性的名称进行兴趣包和潜在的匹配数据在CS和条目FIB的名称。我们描述了模糊兴趣转发的方法,概述了语义理解功能,确定名称匹配,并提出我们的模拟研究与扩展的评估结果。

英文摘要:

In the current Named Data Networking implementation, forwarding a data request requires finding an exact match between the prefix of the name carried in the request and a forwarding table entry. However, consumers may not always know the exact naming, or an exact prefix, of their desired data. The current approach to this problem-establishing naming conventions and performing name lookup-can be infeasible in highly ad hoc, heterogeneous, and dynamic environments: the same data can be named using different terms or even languages, naming conventions may be minimal if they exist at all, and name lookups can be costly. In this paper, we present a fuzzy Interest forwarding approach that exploits semantic similarities between the names carried in Interest packets and the names of potentially matching data in CS and entries in FIB. We describe the fuzzy Interest forwarding approach, outline the semantic understanding function that determines the name matching, and present our simulation study along with extended evaluation results.

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。

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