# IMU: A Content Replacement Policy for CCN, Based on Immutance Content Selection
## 2 Related Work
Filtering out the locally popular contents and placing the popular contents in the privileged partition are the key features of the LFRU eviction policy. This policy demonstrates considerable improvements, but it fails to evict suitable content in the case of bursty requests. Moreover, this technique also requires a large processing time to manage partitions. PPC is a chunk-level in-network caching eviction policy [26]. It is capable of predicting the popularity of the video chunks. PPC stores content based on the popularity that it predicts. On the other side, the contents that have the least popular prediction are evicted out. This eviction scheme is also termed the Assist-predict model. It is based on the request information of the neighboring chunks. It also predicts future popularity by using past experience with the popularity of the content. If the popularity of the new content is less than the former popularity, the newly incoming chunk does not cache. Otherwise, it evicts the future content based on popular prediction. This model-based prediction technique works well but fails to predict properly against frequently changing requests. Moreover, this policy leads to high network load due to control signaling overhead and high computational workload. The NC-SDN eviction model was introduced as a cache eviction algorithm that relies on SDN (software-defined networking) [16]. NC-SDN model uses three arguments. First, it calculates data popularity; second, it comes to know the location of cache management switches; third, it facilitates cooperation among different nodes in the network. When the cache is fully occupied, it checks the popularity of each content and replaces the least popular content with new content. Although the replacement technique is straightforward, the control traffic and exchange of information between the switches are very high, leading to performance losses. LFF is a content replacement policy that predicts the time of the next event [27]. Based on the prediction, it controls the residual life of retrieved content. When the cache capacity is full, this policy measures the time for which the content is considered invalid. In addition, this policy checks whether the source has been updated after retrieving the content to check the validity of each content. This policy ignores the high replacement rate in the central node and does excessive computing, making it impractical for large CCN. NICE has been introduced as a new metric for cache management in ICN [28]. This policy uses a method that computes the centrality. Centralization is used in the replacement phase to manage cache contents. This method is based on the number of caches instead of the number of contents. Content is replaced when the NICE value is high, as the contents move from one cache to the other due to the centrality of the content. However, it causes high network load and computational complexity. Most of the replacement strategies [27; 28; 29; 30; 31; 32; 33; 34] on CCN focus on content frequency, popularity, and time freshness. These policies ignore the concept of content immaturity in content eviction; it is neither popular nor mature when new content is cached in the cache. We need some time to evaluate whether this content has become popular or not. If that content is removed from the cache, the consumer has to retrieve it from the publisher, which affects network performance. Therefore, content may become popular for a certain period, and then its popularity starts decreasing [29]; if that content is not removed from the cache, network performance and the cache hit ratio also degrade. When the cache space is low and the popularity of the content changes frequently, it becomes challenging for the content eviction policy to decide which content should be evicted from the cache space. A content eviction policy should be able to provide equal opportunities for each content to become mature.