An adaptive neuro-fuzzy inference system-based caching scheme for content-centric networking

Abstract

Content-centric networking (CCN) is gradually becoming the alternative approach to the traditional Internet architecture through enlightening information (content) distribution on the Internet with content names. The growing rate of Internet traffic has adapted a content-centric architecture to better serve the user requirement of accessing a content. For enhancing content delivery, ubiquitous in-network caching is utilized to store a content in each and every router by the side of the content delivery path. From the study, it is evaluated that a better performance can be achieved when caching is done by a subset of CRs instead of all CRs in a content delivery path. Motivated by this, we proposed an adaptive neuro-fuzzy inference system-based caching (ANFIS-BC) scheme for CCN to improve the cache performance. The proposed ANFIS-BC scheme utilizes the feature of centrality-measures for selection of a router for caching in a network. Our results demonstrated that the ANFIS-BC scheme consistently achieves better caching gain across the multiple network topologies.