# NDN Content Store and Caching Policies: Performance Evaluation
## 3 In-Network Caching
### Caching Decision Strategy  
The distribution of a cache along the network is decided by the caching decision strategy. Several strategies have been proposed. The main ones are (a) _leave copy everywhere_ (LCE)--when a data packet is sent downstream, a copy of it is cached in all nodes along the path; (b) _leave copy probabilistically_ (LCP)--similar to LCE but the content is cached with a given probability; (c) _leave copy down_ (LCD) [28]--when a content is sent downstream, the cached copy is replicated one hop downstream. This scheme avoids the proliferation of the same content throughout the network; (d) _move copy down_ (MCD) [28]--similar to LCD but, in this case, the strategy _shifts_ the cached copy one hop downstream; (e) _copy with probability_ (_Prob_) [28]--a copy is cached in all downstream nodes but with a probability \(p\). For \(p=1\), this strategy is equal to LCE; (f) _randomly copy one_ (_RCOne_) [29]--similar to LCD but instead of caching in the next hop node, the copy is placed on a randomly selected downstream node; (g) _probabilistic cache_ (_ProbCache_) [30]--caches in the downstream nodes, but with a different probability for each node. The probability of placing a copy in a node closer to the consumer is higher than that far from the consumer; and (h) _WAVE_[31]--similar to LCD but this scheme takes into account the correlation in requests between the different chunks from the same content. A node explicitly suggests to the next downstream node which content should be stored. In this work, as we do not focus on the caching decision, only the LCD strategy is considered. An extension of this work to include other strategies may be considered in the future.  
The content popularity is another factor contributing to caching performance. Some proposals on categorizing the content popularity can be found in [4]: (a) popularity over web proxy [32]; (b) popularity over hierarchy of proxies [33; 34]; (c) popularity in _peer-to-peer_ (P2P) networks [35]; (d) popularity of _video on demand_ (VoD) [36]; and (e) popularity of user-generated content (video on Youtube) [37; 38; 39]. According to these studies, the _Zipf-Mandelbrot_ (MZipf) distribution [32] is the most suitable model to represent the content popularity for web requests. The probability of accessing an object at rank \(i\) out of \(N\) is given by (1)  
\[p(i)=\frac{K}{(i+q)^{\alpha}} \tag{1}\]  
where \(K\) is given by  
\[K=\sum_{c=1}^{N}\frac{1}{(c+q)^{\alpha}}. \tag{2}\]  
The skewness factor (\(\alpha\)) is used to control the slope of the distribution curve, and \(q\geq 0\) defines the flatness of the curve. The higher the skewness factor \(\alpha\), the higher the CHR. We chose two values for \(\alpha\): one observed in traces for web proxies (\(\alpha=1.1\)) which mimic an ISP network, and its content distribution follows the heavy-tailed distribution [33]; the other for user-generated web content (\(\alpha=0.75\)) [32]. The reasoning behind this choice is the need to study the influence of different CS levels for different content types.  
Within the scope of strategies based on popularity, some additional proposals can be found in such works as [40; 41; 42; 43; 44; 45; 46].