Comparing apples to apples in ICN
Comparing apples to apples in ICN
复制标题
ICN 中的苹果与苹果的比较
DOI：
10.1109/ccnc.2017.7983087
复制
发表时间：
2017
期刊：
影响因子：
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通讯作者：
Urs Schnurrenberger
中科院分区：
文献类型：
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作者：
Urs Schnurrenberger
ICN中的许多算法和应用程序直接依赖于内容名称。更令人惊讶的是，没有共同的基础来评价它们。没有共同的基础，苹果就被比作橘子。由于本文的目标是在国际竞争网络中实现真实的、现实的和可比较的评价，因此还需要比较内容名称集的方法。有两种方法可以达到这个目标：一个标准的内容名称集合，或者一个数据集特征的抽象描述，以便于比较。我们为这两种方式提供解决方案。我们提出的内容名称集合（CNC），收集的内容名称的基础上真实的数据的派生。我们通过经验观察获得普遍的见解。在此基础上，介绍并讨论了两种可能的真实描述数据集特征的数学抽象。通过对偏态正态分布的公式进行稍微扩展，我们发现了一个支持我们目标的有力候选者。此外，所有的抽象不仅可以用来描述现有的数据集，而且还可以用于数据集的真实模拟。
Many algorithms and applications in ICN directly depend on content names. All the more surprising that there is no common foundation to evaluate them against each other. Without a common foundation, apples are compared to oranges. As this paper has the goal to enable real, realistic and comparable evaluations in ICN, also methods to compare sets of content names are needed. There are two ways to reach that goal: A standard collection of content names, or an abstract description for data set characteristics, facilitating comparison. We provide solutions for both ways. We present the derivation of the Content Name Collection (CNC), a collection of content names based on real data. We gain generalized insights through empirical observations. On this foundation, two possible mathematical abstractions realistically describing data set characteristics are introduced and discussed. With a slightly extended formula for skewed normal distributions, we found a potent candidate supporting our goals. Moreover, all abstractions can be used not only to describe existing data sets, but also for realistic simulations of data sets.