Identifying Common Periodicities in Mobile Service Demands with Spectral Analysis

Cristina Marquez (1) , Marco Gramaglia (1) , Marco Fiore (2) , Albert Banchs (2, 1) , Zbigniew Smoreda (3)

1 UC3M - Universidad Carlos III de Madrid [Madrid]
2 Institute IMDEA Networks [Madrid]
3 Orange Labs [Issy les Moulineaux]

In this paper, we investigate the existence and prevalence of comparable dynamics in the temporal fluctuations for the traffic demands generated by mobile applications. To this end, we hinge upon a spectral analysis framework, by computing Discrete Fourier Transforms of the typical demands for tens of popular mobile services observed in an operational metropolitan-scale network. We filter, cluster, and analyse hundreds of frequency components, and identify a substantial set of regular patterns that are common across most service demands. We also unveil how several mobile services defy classification, and have instead highly distinguishing temporal dynamics.

Domaines: Réseaux et télécommunications [cs.NI]

Cristina Marquez, Marco Gramaglia, Marco Fiore, Albert Banchs, Zbigniew Smoreda. Identifying Common Periodicities in Mobile Service Demands with Spectral Analysis. IEEE MedComNet, Jun 2020, Arona, Italy. ⟨hal-02904052⟩