# Content Management Based on Content Popularity Ranking in Information-Centric Networks
## 2 Related Work
### Content Caching and Removal Schemes  
The basic CCN uses the ALWAYS caching scheme, which is the same as the Leave Copy Everywhere (LCE) scheme that means all requested content is replicated in all nodes on the transmission path. As this causes considerable cache redundancy, the caching performance was improved in [21] by incorporating the Leave Copy Down (LCD) caching policy where the content is cached only at the next-hop node from the content-access node. The Move Copy Down (MCD) caching policy was proposed to enhance the caching performance further where the original content at the content-access node is deleted after the content is cached at the next-hop node. Caching the most popular content at the nearest machine utilizing CCN was proposed by [22; 23]. Then, to reduce caching costs and share the load among the nodes, Prob [24] was proposed. Similarly, ProbCache [25] introduced content caching scheme based on the remaining storage capabilities of the nodes. Other than CCN, NDN also provides a cache storage mechanism at the intermediate nodes; therefore, the concept of identifying and caching popular content works for NDN as well. Based on a compound popular content caching strategy (CPCCS), content caching mechanism was proposed for NDN in [26] that selects an optimal popular content for caching by calculating the number of requests content received. Later on, another content caching mechanism based on compound popularity was proposed for NDN in [27]. That scheme tried to increase the utilization of the existing content by considering the popularity of the content and the node popularity simultaneously. In addition, [28] proposed a new caching strategy named Most Interested Content Caching (MICC) that enhances the content distribution by caching the requested content near the consumers at various appropriate locations. Furthermore, [29] proposed another content caching scheme named Efficient Hybrid Content Placement (EHCP) to reduce the duplications of homogeneous content at several locations. This scheme also looked to increase the content diversity along the transmission path. A periodic caching strategy was proposed in [30] for the IoT environment based on NDN. The study provided simulation results in terms of content placement strategies and stretch, one of the standard performance metrics, and showed that the proposed method decreases the content retrieval time and improves the cache-hit ratio. Besides these content caching ideas, cache replacement schemes are getting attention from researchers as well. These schemes remove one or more types of content from the repositories when there is not enough space for caching new content. The Least Recently Used (LRU) [31] scheme replaces the least recently used storage unit, which means that the content that has not been accessed for the longest time is replaced. The Least Frequently Used (LFU) [32] scheme replaces the storage unit with the least access times, meaning that the content with the least number of access requests is replaced. These two basic cache replacement schemes are simple in concept, and they are implemented regularly. Features of these two schemes are analyzed in [33]. Another basic scheme that simply replaces the oldest content from the repository is called First In First Out (FIFO). Random (RAND) [34] policy removes content randomly from the storage, which may be proved to be an inefficient policy, as popular content may be removed to store less popular content. Besides proposing a content popularity-based cache resolution scheme, a cache replacement scheme based on content age is also proposed in [35] to reduce network delay and redundancy. The pivotal concept is having a basic age and a maximum age for all the content and removing the content when the age becomes zero over time, otherwise removing the content with the lowest age if there is a lack of space.