Interactive app traffic: An action-based model and data-driven analysis

Many popular smartphone apps involve human interaction through the entire session; e.g. apps for web browsing, making reservations and online gaming. In this work, we characterize bi-directional interactive app traffic in the timescale of seconds, that is shaped by the human interaction. We collect and analyze a dataset comprising 1500 interactive app sessions. The combined uplink and downlink traffic bursts are the outcome of user-server interactions, which we label as actions. Within each action, we discover high correlation between the number of uplink and downlink packets reaching 0.98. Our study reveals that the distribution of action duration and interarrival can be approximated with exponential or gamma distributions. The analysis provides insights on the temporal characteristics of bi-directional packet bursts associated with actions, during an app session. We show that action-based service at access points (APs), where actions constitute the service units rather than packets, can reduce service delay by 50%.

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