# Performance Analysis of Cache Based on Popularity and Class in Named Data Network
## 2 Basic of Cache Based on Popularity and Class
### System Model  
In accordance with Figure 2, the network model is made with N router members. The set member is routers \(r_{n}\), where \(n=1,2,3\),..., \(N\). Router \(r_{1}\), also known as an edge router, is the closest router to the consumer, while router \(r_{N}\) is the closest router to the producer. Parameter \(c_{kc}\) is specified as the cache size for class \(c\), on the router \(r_{k}\). The consumer demand is modeled according to the results of several previous studies. [PERSON] et al. provide measurement results of consumer's request to the web traffic services, User Generated Content (UGC), and video content [22]. All requests can be modeled following the Zipf-like distribution with a similar exponent factor value, which is approximately 0.8. RFC7945 used to determine the variation of the Zipf-Mandelbrot distribution. Zipf-Mandelbrot provides a better model for the environment where the node can store the requested content locally [23]. Studies on the characteristics of traffic also explain that the web content population is the largest, followed by User Generated Content (UGC) and video on demand (VoD) content [22]. One other study of the demand model was carried out by [PERSON] et al. [24] on modeling a document request based on the Poisson distribution with an average value of the request at a certain time. The demand model is as shown in Figure 3, where the average number of requests for a content service class follows the Poisson distribution with the request rate \(\lambda_{k}\) for class \(k\), which is follows the Zipf-Mandelbrot distribution with exponent factor \(\alpha_{k}\) for class \(k\). Figure 1: Flowchart of Cache Based on Popularity and Class (CAPIC) caching mechanism. Figure 2: Network model. Contents on the network are divided into several classes according to their characteristics [17]. The content class classification is based on delay and jitter requirements, content usage duration, and the request rate pattern at a certain interval for each class. The first class requires low delay and low jitter with the contents repeatedly requested by many users in years, and the request rate is medium relative to other classes. The second class is User Generated Content (UGC), with a the medium requirement delay and less jitter. UGC comprises of any content that has been created and published by unpaid contributors, such as blogs, social media content, etc. It has a medium content use duration, and in most situations, it is repeatedly request for a few weeks. Furthermore, it has a high request rate relative to other content classes. The third content class is a web application, which has the same requirement of delay and jitter as the second class, and a higher usage period than the first and second class. The first class has the highest priority in terms of the delay and jitter requirements, followed by the second and third classes. Table 1 shows that there are four content service classes with three service classes that need to be cached, while one does not require cache system. The Cache Based on Popularity and Class (CAPIC) algorithm pays attention to content classification procedure to determines the amount of content that can be stored on the content store. In this research, the number of requests for a content service class follows the Poisson distribution. The Zipf-Mandelbrot distribution, which contains a flattened factor, is used to model requests for data due to content popularity [24; 25; 26]. Content on the top rankings has a greater probability of being requested by consumers compared to lower rankings. The probability of consumers accessing content \(i\) is expressed by (1):  
\[p\left(i\right)=\frac{\left(i+q\right)^{-\alpha}}{\sum_{i=1}^{N} \left(i+q\right)^{-\alpha}} \tag{1}\]  
where: