the quantity of CPU required by gaming applications significantly depends on such factors as the actions performed by the players – a fact that leads to a larger variability.

The fact that all fitted relationships are linear is not especially relevant [*per se*]{}, nor particularly surprising; indeed, it makes intuitive sense that the amount of work needed to produce a certain type of data grows, more or less linearly, with the quantity of data to produce. What matters the most is the [*slope*]{} of the fitted lines, which changes by several orders of magnitude from one traffic category to another.

In the following, we will indicate with $\tau_k$ the number of CPU ticks needed to process one megabyte of traffic of category $k$; for instance, $\tau_\text{video}=0.25$ CPU ticks/Mbyte (i.e., the slope of the red line in [Fig. \[fig:scatter-video\]]{}), $\tau_\text{gaming}=161.38$ ticks/Mbyte (i.e., the slope of the red line in [Fig. \[fig:scatter-gaming\]]{}), and $\tau_\text{maps}=67.44$ ticks/Mbyte (i.e., the slope of the red line in [Fig. \[fig:scatter-maps\]]{}).

Enriching the dataset {#sec:sub-enrich}
---------------------

As a preliminary step, we need to decide, for each app we observe in the dataset, whether its traffic can be considered video-like, gaming-like, or map-like (or none of them). We perform 