Comprehensive analysis of caching performance under probabilistic traffic patterns for content centric networking

Abstract

The phenomenon of data explosion represents a severe challenge for the upcoming big data era. However, the current Internet architecture is insufficient for dealing with a huge amount of traffic owing to an increase in redundant content transmission and the end-point-based communication model. Information-centric networking (ICN) is a paradigm for the future Internet that can be utilized to resolve the data explosion problem. In this paper, we focus on content-centric networking (CCN), one of the key candidate ICN architectures. CCN has been studied in various network environments with the aim of relieving network and server burden, especially in name-based forwarding and in-network caching functionalities. This paper studies the effect of several caching strategies in the CCN domain from the perspective of network and server overhead. Thus, we comprehensively analyze the in-network caching performance of CCN under several popular cache replication methods (i.e., cache placement). We evaluate the performance with respect to well-known Internet traffic patterns that follow certain probabilistic distributions, such as the Zipf/Mandelbrot-Zipf distributions, and flash-crowds. For the experiments, we developed an OPNET-based CCN simulator with a realistic Internet-like topology.