Characterizing and Modeling Session-Level Mobile Traffic Demands from Large-Scale Measurements

Abstract

We analyze 4G and 5G transport-layer sessions generated by a widerange of mobile services at over 282, 000 base stations (BSs) of anoperational mobile network, and carry out a statistical characteriza-tion of their demand rates, associated traffic volume and temporalduration. Our study unveils previously unobserved session-levelbehaviors that are specific to individual mobile applications andpersistent across space, time and radio access technology. Basedon the gained insights, we model the arrival process of sessions atheterogeneously loaded BSs, the distribution of the session-levelload and its relationship with the session duration, using simpleyet effective mathematical approaches. Our models are fine-tunedto a variety of services, and complement existing tools that mimicpacket-level statistics or aggregated spatiotemporal traffic demandsat mobile network BSs. They thus offer an original angle to mobiletraffic data generation, and support a more credible performanceevaluation of solutions for network planning and management. Weassess the utility of the models in practical application use cases,demonstrating how they enable a more trustworthy evaluation ofsolutions for the orchestration of sliced and virtualized networks.