IMU: A Content Replacement Policy for CCN, Based on Immature Content Selection

Abstract

In-network caching is the essential part of Content-Centric Networking (CCN). The main aim of a CCN caching module is data distribution within the network. Each CCN node can cache content according to its placement policy. Therefore, it is fully equipped to meet the requirements of future networks demands. The placement strategy decides to cache the content at the optimized location and minimize content redundancy within the network. When cache capacity is full, the content eviction policy decides which content should stay in the cache and which content should be evicted. Hence, network performance and cache hit ratio almost equally depend on the content placement and replacement policies. Content eviction policies have diverse requirements due to limited cache capacity, higher request rates, and the rapid change of cache states. Many replacement policies follow the concept of low or high popularity and data freshness for content eviction. However, when content loses its popularity after becoming very popular in a certain period, it remains in the cache space. Moreover, content is evicted from the cache space before it becomes popular. To handle the above-mentioned issue, we introduced the concept of maturity/immaturity of the content. The proposed policy, named Immature Used (IMU), finds the content maturity index by using the content arrival time and its frequency within a specific time frame. Also, it determines the maturity level through a maturity classifier. In the case of a full cache, the least immature content is evicted from the cache space. We performed extensive simulations in the simulator (Icarus) to evaluate the performance (cache hit ratio, path stretch, latency, and link load) of the proposed policy with different well-known cache replacement policies in CCN. The obtained results, with varying popularity and cache sizes, indicate that our proposed policy can achieve up to 14.31% more cache hits, 5.91% reduced latency, 3.82% improved path stretch, and 9.53% decreased link load, compared to the recently proposed technique. Moreover, the proposed policy performed significantly better compared to other baseline approaches.