difference whether the data is consumed by a small number of users enjoying a high bandwidth, or through a larger number of lower-rate connections. [*None*]{} of the currently available real-world traces contain all the information needed to distinguish these cases.

We fill this gap by developing and demonstrating a methodology to [*enrich*]{} existing demand traces with computational power information, translating (so to say) megabits of downloaded data into CPU ticks consumed at the servers. Specifically, in this paper we (i) consider a large-scale, crowd-sourced cellular demand trace, already used in big data applications [@scott]; (ii) perform an extensive set of experiments, linking the quantity of downloaded data in different conditions (type of service, number of users,...) to the amount of CPU power required at the servers; (iii) assess the impact of enriching our traces on the resulting planning of the MEC network. Importantly, we make [*publicly available*]{} realistic user demand traces, as well as our experimental dataset linking user demand to computational burden [@html].

We stress that, although MEC was our original motivation and we use it as a test case, the problem we address is more fundamental and consistent with the high-level goal of big data research, that is, 