load. Gaming and maps represent a small fraction of the total traffic ([Fig. \[fig:pies\]]{}(left)); however, by applying [(\[eq:model\])]{}, we obtain the results shown in [Fig. \[fig:pies\]]{}(right): gaming applications consume the vast majority, over 90%, of all computational resources. This further highlights the importance of accounting for the computational load of different types of traffic in network design.

![ Categories traffic demand belongs to (left); amount of computational power required to serve video-, and gaming- and maps-like traffic (right). \[fig:pies\] ](img/pie_traffic "fig:"){width=".23\textwidth"} ![ Categories traffic demand belongs to (left); amount of computational power required to serve video-, and gaming- and maps-like traffic (right). \[fig:pies\] ](img/pie_ticks "fig:"){width=".23\textwidth"}

Discussion {#sec:sub-discuss}
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Our trace enrichment strategy has some limitations: it does not account for all the traffic types, and might not perfectly model the behavior of all servers.

The first issue is evident from [Fig. \[fig:pies\]]{}(left): around 25% of all traffic cannot be classified as either video-like, gaming-like, or maps-like. On the other hand, our analysis is able to account for over 70% of all present-day traffic, and video, gaming and maps are the applications that are expected to grow the most in the near future.

The second issue has to do with the 