# Request Expectation Index Based Cache Replacement Algorithm for Streaming Content Delivery over ICN
## 1 Introduction  
Currently, the main usage of the Internet is gradually shifting from connection-driven communications, such as end-to-end conversations, to information-driven communications, e.g., content broadcasting and retrieval [1]. To cater to this trend, information oriented future internet architectures have been proposed, such as Data-Oriented Network Architecture (DONA), Publish-Subscribe Internet Technologies (PURSUIT), Network of Information (NetInf), Content-Centric Networking (CCN) and Named Data Networking (NDN) [2; 3; 4; 5; 6; 7; 8]. The approach of these architectures is commonly called Information-Centric Networking (ICN) [9; 10]. ICN enables transferred information (content) to be named uniquely at the network layer so that the named content can be forwarded directly without any location information, namely the IP address. This mechanism favors the deployment of in-network caching, content multicasting, content-level security, etc. Accordingly, ICN is capable of facilitating the efficiency of data transmission from the source to the end users. To further enhance the efficiency of content delivery, in-network caching is adopted, which has become a distinct research field of ICN [11; 12]. The named content in ICN can not only be transferred but also be temporarily stored in each network device for directly responding to the potentialsubsequent requests. Precisely, in-network caching studies focus on the management of cached content and aim to shorten the delivery distance between the content source and requesters to enhance the performance of content delivery [13]. Therefore, an appropriate cache management scheme can significantly improve the network performance. Cache management schemes can be classified into cache decision schemes and cache replacement algorithms. The cache decision scheme focuses on choosing appropriate caching locations in the network for particular contents to reduce the cache redundance and to improve the cache utilization. Recent cache decision studies mainly depend on the content popularity [14; 15; 16; 17; 18; 19], node features, such as node capability and importance [20; 21; 22; 23], and cache coordination [24; 25; 26]. In addition, a per-fetching based cache decision scheme specifically for adaptive video streaming is proposed in [27], which aims to maintain high quality video playback to the clients. A cache replacement algorithm is necessary for each ICN router when the cache decision scheme is processed. For instance, Leave Copy Everywhere (LCE) and Least Recently Used (LRU) are adopted to work cooperatively as the default cache management schemes of NDN, as shown in Figure 1. The cache replacement algorithm for ICN refers to the process that evicts a selected piece of content in the storage space to make room for incoming content. Cache replacement is not a fresh issue raised by ICN. For memory management in computers, cache replacement algorithms are designed to improve memory page utilization. LRU, Least Frequently Used (LFU), First-In and First-Out (FIFO) and the Optimal replacement algorithm (OPT) [28] (also named [PERSON]'s algorithm) have been proposed to overcome the access speed difference between the slow auxiliary storage devices and the fast cache memory. Among these cache replacement algorithms, the OPT algorithm is the most inspiring one and has been proven to be theoretically optimal [29; 30]. The OPT algorithm evicts the item that will not be accessed for the longest period of time. However, this theoretically optimal algorithm is rarely implementable because it is difficult to predict when the cached items will be accessed in the future. In addition, the cache replacement issues for web caching scenarios are well studied [31; 32]. These studies can be classified as recency-based replacement (LRU, HLRU, Pyramidal Selection Scheme (PSS), etc. ), frequency-based replacement (LFU, LFU-Aging, etc. ), function-based replacement (Greedy Dual (GD)-Size, Taylor Series Prediction (TSP), etc.) and randomized-based replacement (RAND, etc.) [33].