AdCaS: Adaptive Caching for Storage Space Analysis Using Content Centric Networking

From last few years, an exponential growth in content sharing among smart devices results in an extra burden on the underlying communication infrastructure. The traditional host-based architecture is not sufficient to serve the storage and access requests of such an enormous amount of content. Consequently, a more flexible, content-centric networking (CCN) approach was introduced which decouples the content from its physical location. In CCN architecture, each network device is capable of serving as data provider. However, network performance is constrained by the limited caching capacity of network devices. For efficient implementation of any solution, fast caching on each smart device is required. Hence, in this work, we propose a new Count-Min-Sketch based adaptive caching mechanism, which caches the content based upon content popularity and its distance from the requesting node with an aim to maximize the hit ratio and minimize the overall operational cost. We also propose the usage of Bloom Filter for storing the cached content and maintaining the routing table node for reduction of storage and access cost. The proposed scheme is evaluated with respect to the performance parameters where its performance is found superior in comparison to the existing competing schemes of its categories.