Optimal Content Prefetching in NDN Vehicle-to-Infrastructure Scenario

Abstract

Data replication and in-network storage are two basic principles of the information-centric networking (ICN) framework in which caches spread out in the network can be used to store the most popular contents. This paper shows how one of the ICN architectures, i.e., Named Data Networking (NDN), with content prefetching can maximize the probability that a user retrieves the desired content in a vehicle-to-infrastructure scenario. We give an integer linear programming formulation of the problem of optimally distributing content in the network nodes while accounting for the available storage capacity and the available link capacity. The optimization framework is then leveraged to evaluate the impact on content retrievability of topology- and network-related parameters as the number and mobility models of moving users, the size of the content catalog, and the location of the available caches. Moreover, we show how the proposed model can be modified to find the minimum storage occupancy to achieve a given content retrievability level. The results obtained from the optimization model are finally validated against an NDN architecture through simulations in ndnSIM.