Analytical Modelling of Content Transfer in Information Centric Networks
Xu Han, Haozhe Wang, Jia Wen Hu, Geyong Min · 2021
The proliferation of advanced information technology applications such as Virtual/Augmented Reality and ultra-high-definition (UHD) multimedia services that demand high bandwidth and ultra-low latency put tremendous pressure on the current communication networks. To meet these pressing requirements, Information-Centric Networks (ICN), a promising future Internet paradigm has been attracting much research attention. ICN deploy ubiquitous in-network caching that could not only handle large content dissemination and retrieval but also expedite the content delivery. To investigate the performance of ICN, it is important to have an analytical model that can accurately characterize the content transfer in ICN under different network and traffic conditions. In this paper, we exploit the queueing network theory to develop a new analytical model for content transfer in ICN. We derive the mathematical expressions for calculating cache miss rate and content delivery time. The accuracy of our analytical model is validated by comparing the analytical results with those obtained from simulation experiments. We also use the model to investigate the content delivery time under various network and traffic conditions.