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Vehicular users are expected to consume large amounts of data, for bothentertainment and navigation purposes. This will put a strain on cellularnetworks, which will be able to cope with such a load only if proper caching isin place, this in turn begs the question of which caching architecture is thebest-suited to deal with vehicular content consumption. In this paper, weleverage a large-scale, crowd-collected trace to (i) characterize the vehiculartraffic demand, in terms of overall magnitude and content breakup, (ii) assesshow different caching approaches perform against such a real-world load, (iii)study the effect of recommendation systems and local contents. We define aprice-of-fog metric, expressing the additional caching capacity to deploy whenmoving from traditional, centralized caching architectures to a "fog computing"approach, where caches are closer to the network edge. We find that forlocation-specific contents, such as the ones that vehicular users are mostlikely to request, such a price almost disappears. Vehicular networks thus makea strong case for the adoption of mobile-edge caching, as we are able to reapthe benefit thereof -- including a reduction in the distance traveled by data,within the core network -- with little or no of the associated disadvantages.

The Price of Fog: a Data-Driven Study on Caching Architectures in Vehicular Networks