A fairness analysis of content centric networks

In recent years the new vision of data-centric networks has emerged as a natural way to satisfy user needs in the Future Internet. It is based on novel architectures oriented towards data sharing and delivering as opposite to classic host-to-host communications. Among several proposals, Content Centric Networking has been conceived as a very promising solution by the Palo Alto Research Center team. It can be gradually deployed over current IP networks and solves different problems, as NAT Traversal, depletion of IP addresses, security, mobility, and multicast communications. In this work an analytical framework for investigating properties of Content Centric Network is proposed, with a particular focus on fairness in cache usage. It captures the distribution of content replicas among nodes of the network by taking into account contents availability, data popularity, topology information, and cache size. Preliminary results, validated using numerical simulations, shed some light on the underlying fairness of a Content Centric Network, thus helping future developments and upgrades on the current architecture.