# NDN Content Store and Caching Policies: Performance Evaluation
## 4 Related Work  
As the performance of the caching system is a very important factor to support the content-centric networks, and one that can affect the entire network performance, it has been the focus of attention by the networking research community. Not intending to be exhaustive, the following are some of the related works found in this area. In [50; 51], a caching replacement policy dubbed _universal caching_ (UC) was proposed. The proposal was evaluated and compared to FIFO and LRU. The authors concluded that the proposal presents better performance, even in situations where the CS size and topology are variable (location of consumers and producers in the network). The study was performed using two scenarios: (1) a synthetic topology based on the _Watts-Strogatz_ model [52], more suitable for networks with high density but short range, as in the case of sensor networks; and (2) _spring_ topology [53], more suitable for the _interter service provider_ (ISP). In [5], the influence of number of CS on the performance of the network was investigated. The study used two network topologies suitable for ISP (e.g., Abilene [54] with 11 nodes, and GEANT with 42 nodes) and compared the performance of LFU in a topology with 20%, 50%, 80% and 100% of CS. The study concluded that the network performs better when the network has around 40% of CS; above this threshold, the performance drops. The study [7] proposed _content caching popularity_ (CCP), a replacement policy based on content popularity. The authors based their study on a network where the consumers and producer are placed on the edge of the network. They evaluated and compared the performance of LRU and LFU in terms of CHR, server load, and average network transfer rate. The work in [55] evaluated the gain of caching strategies, comparing the placement of CS at the edge or distributed pervasively. The authors concluded that the difference in these two approaches is relatively low, around 9%, which significantly reduces to around 6% when increasing the size of edge caching. In [6], _networking cache-SDN_ (NC-SDN), a centralized caching replacement strategy based on _software-defined networks_ (SDN) [56] and popularity was proposed. The popularity is computed by switches, based on the number of occurrences of a certain prefix in FIB. The study performed a comparative study with LFU, LRU, FIFO and NDNS [57], and concluded that NC-SDN is better than the others in terms of CHR and bandwidth occupation. The authors in [13] proposed _PopuL_, a cache replacement policy based on content popularity and the betweenness of nodes in the network. An intra-domain resource adaptation resolving server was used to store the cache status. Using CHR metric, the proposal was evaluated and compared with LRU, LFU and FIFO. The authors concluded that _PopuL_ presents better performance. The authors in [12] proposed _PopNetCod_, a popularity-based caching policy that decides on caching or evicting a content based on the received interest packet instead of the corresponding data packet, that is, the decision of caching is made while processing the received interest before the corresponding data packet arrives at the router. _PopNetCod_ includes a caching decision strategy and a cache replacement policy. The proposal was evaluated and the cache replacement policy was compared with LRU. The authors concluded that _PopNetCod_ is better than the others in terms of the chosen metrics. A popularity-based cache policy for privacy-preserving in NDN (PPNDN) was proposed by [10]. The solution is designed to prevent cache privacy violation, resorting to the cache expiration time, which is set according to the number of content requests. The cache probability of the content in PPNDN is calculated based on the content popularity and the router information. The proposal is evaluated (based on the CHR, cache storage time, and average load of content provider), and its performance is compared with LRU and FIFO. The authors concluded that PPNDN is better than the others in terms of the chosen metrics.