such an assignment as follows:

-   traffic coming from YouTube, Netflix, TimeWarner, ShowBox, Twitch, DirectTV, FoxSports, FoxNews, is tagged as video-like;

-   traffic coming from Minecraft, World of Warcraft, Riptide, Grand Theft Auto, Rollercoaster Tycoon, This War of Mine, Titan Quest, Unkilled, is tagged as game-like;

-   traffic coming from Google Maps and Waze is tagged as map-like.

As summarized in [Fig. \[fig:pies\]]{}(left), about 66% of the traffic we observe in our dataset can be classified as video-like, about 15% of it as game-like, while the amount of map-like traffic is much smaller. Much of the traffic that cannot be tagged comes from social networking applications, whose behavior cannot be easily studied due to the lack of open-source social network servers.

Once the assignment is made, we can use the relationship in [(\[eq:model\])]{}, learned in [Sec. \[sec:sub-learn\]]{}, to add a new column to our dataset. The column, called , expresses how much computational power is required to generate the traffic reported in each line of the dataset. Notice that relationships other than linear can be accounted for at no additional complexity.

[Fig. \[fig:pies\]]{} further highlights the gap between the quantity of traffic generated by different applications and the corresponding CPU 