The impact of vehicular traffic demand on 5G caching architectures: A data-driven study

In this work, we exploit a real-world, large-scale trace coming from the users of the We-Fi app in order to (i) understand how significant the contribution of vehicular users is to the global traffic demand; (ii) compare the performance of different caching architectures; and (iii) studying how such a performance is influenced by recommendation systems and content locality. We express the price of “fog computing” through a metric called price-of-fog, accounting for the extra caches to deploy compared to a traditional, centralized approach. We find that “fog computing” allows a very significant reduction of the load on the core network, and the price thereof is low in all cases and becomes negligible if content demand is location specific. We can therefore conclude that vehicular networks make an excellent case for the transition to mobile-edge caching: thanks to the peculiar features of vehicular demand, we can obtain all the benefits of “fog computing”, including a reduction of the load on the core network - reducing the disadvantages to a minimum.