# Weighted-CAPIC Caching Algorithm for Priority Traffic in Named Data Network
## 1 Introduction  
Global internet traffic continues to increase every year. Based on Cisco's internet report in 2020, it is estimated that 67% of the world's population will be connected to the internet by 2023, meaning there will be 5.3 billion internet consumers. This number is an increase from a total of 3.9 billion consumers in 2018 [1]. Based on the Global Internet Phenomena Report 2019 by Sandvine, traffic related to streaming media is the highest, followed by user-generated content (UGC) and web access [2]. The current internet uses TCP/IP, a host-based network. In this architecture, consumer requests are addressed to a specific server address. This address is included in the request packet that the consumer sends. Only the destination server can respond to the consumer's request. The traffic growth cannot be supported efficiently by TCP/IP network. The network becomes burdened by the communication process. Named Data Network (NDN) is a content-based architecture that focuses on the name of the content. Content names are used at the application level, and the other layer is used for caching, forwarding, routing, and other data processing [3]. The NDN is suitable for communication needs that are basically focused on content. In contrast to IP that cannot support this naturally, NDN makes communication run more efficiently. NDN is ready for a consumer character with dynamic demand. Another advantage is that we can treat traffic classes differently to give higher performance to the higher-priority classes based on the caching rules. Caching as one of the primary and crucial components in NDN is a solution of bandwidth usage, server load, and service time [4]. Previous research has showed that algorithms with a different treatment for contents provide better performance. [PERSON] et al. proposed treatment techniques for different service classes, both with forwarding techniques and caching techniques [5]. The service is divided into four classes, and there are differentpolicies for each class. [PERSON] et al. proposed partition-based caching that gives separated space for contents and simulated some cases of partition with a particular forwarding algorithm [6]. [PERSON] et al. proposed utility-driven caching that provides different allocation for content providers and content publishers based on the maximum utility [7]. Split-caching presented by [PERSON] et al. used the technique to divide the content store into two parts, for priority and nonpriority content. The algorithm decides whether the content needs to be cached or not [8]. [PERSON] et al. proposed Dynamic-CAPIC, a caching algorithm that distinguishes the cache portion based on the popularity and class of the content in real-time [9]. This algorithm increases the network cache hit ratio (CHR) and provides performance that matches a particular class on the system. However, we need to develop a technique to give the best performance for the main priority class while increasing total system performance by efficiently using all available resources on the network, to provide a better experience for the consumer. In this research, we propose Weighted-Cache based on Popularity and Class (Weighted-CAPIC) as a solution to provide a proper performance for priority class. We derive the utility function formula for multiclass content to obtain the relationship between weight and cache hit ratio (CHR). This algorithm is suitable for the NDN mobile node because every router node can run this algorithm individually without coordinating with other nodes. This research is written in the following order. Section 2 explains the basics of Named Data Networking. Section 3 describes the research related to caching algorithms on Named Data Network. Section 4 discusses our proposed caching algorithm, Weighted Cache based on Popularity and Class (Weighted-CAPIC) algorithm. Section 5 explains the system model, while Section 6 concludes the simulation results and analysis. Section 7 is about algorithm complexity. Section 8 is the conclusion, and Section 9 is the future research.