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 work shows how one of the ICN architectures, the Named Data Networking (NDN), with content pre-fetching can maximize the probability that a user retrieves the desired content in a Vehicle-to-Infrastructure scenario. We give an ILP 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 a Name Data Networking architecture through simulations in ndnSIM.
Abstract-Information Centric Networking (ICN) is a networking framework for content distribution. The communication is based on a request/response model where the attention is centered on the content. The user sends interest messages naming the content it desires and the network chooses the best node from which delivers the content. This way for retrieving contents naturally fits a context where users continuously change their location. One of the main problems of user mobility is the intermittent connectivity that causes loss of packets. This work shows how in a Vehicle-to-Infrastructure scenario, the network can exploit the ICN architecture with content pre-distribution to maximize the probability that the user retrieves the desired content. We give an ILP formulation of the problem of optimally distributing the contents in the network nodes and discuss how the system assumptions impact the success probability. Moreover, we validate our model by means of simulations with ndnSIM.
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