# Performance Analysis of Cache Based on Popularity and Class in Named Data Network
## 1 Introduction
The data is saved based on Time to Live (TTL) for each application type, with several methods analyzed by setting a static memory allocation, determining the priority application, and calculating the amount of allocated memory based on the application's weight. In addition, the other class can utilize a proportion, when the capacity is not full. The other research related to the traffic characteristics were explained by [PERSON] et al. [16; 17]. They stated that there are variations in requirements and traffic patterns on the network. The 2020 Mobile Internet Phenomena Report shows that approximately 65% of the worldwide mobile downstream traffic is video related-content, and 12.7% is social networking traffic [18]. Therefore, the process of selecting traffic that needs to be cached is still an open issue. The development of caching algorithms until 2019 focused on popularity and probabilistic-based caching [19]. Presently, there are no caching techniques that pay attention to the popularity and exploit the characteristics of content class to maximize network cache hit ratio. NDN routers require the caching method to autonomously and independently determine where the content is to be cached and also the portion. The role is potent in influencing cache hits and hop lengths, which affect network delay and load. Therefore a technique capable of differentiating and optimizing the treatment based on the content classes is needed. This technique is expected to consider the popularity of the content in determining the cache proportion at the router's content store, in order to provide better performance in NDN. This research aims to develop a new caching algorithm, named Cache Based on Popularity and Class (CAPIC), that is capable of providing answers to the problems previously described and improve the overall NDN network performance. The detailed mechanism of the CAPIC algorithm is explained in the next section. Two different mechanisms were applied to the CAPIC algorithm, namely Static and Dynamic. The purpose of Static-CAPIC is to enhance the total network cache hit ratio using the simple technique [20]. We explain Static-CAPIC mathematically and analyze its performance. Then we define the improvement to Dynamic-CAPIC to accommodate the demand more flexibly. The Static-CAPIC and Dynamic-CAPIC are similar at the global mechanism, but they are different in cache proportion determination. Static-CAPIC has the static proportion for cache, while Dynamic-CAPIC specifies the cache proportions dynamically and in accordance with the Dynamic-CAPIC proportion formula. The proportion can change every time according to the actual consumer request rate. This research is written in the following order. Section 2 discusses the general scheme of Cache Based on Popularity and Class (CAPIC). Section 3 explains the Static-CAPIC, including mathematical analysis for the partitioned content store. Section 4 describes Dynamic-CAPIC, a modification of Static-CAPIC with a dynamic cache proportion computation mechanism for each content service class. Section 5 simulates the model and test results, while Section 6 concludes the research. Section 7 is future research.