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Characterizing and Modeling Control-Plane Traffic for Mobile Core Network

Jiayi MengJingqi HuangY. Charlie Hu ...+3 Abhigyan Sharma
Dec 2022
In this paper, we first carry out to our knowledge the first in-depthcharacterization of control-plane traffic, using a real-world control-planetrace for 37,325 UEs sampled at a real-world LTE Mobile Core Network (MCN). Ouranalysis shows that control events exhibit significant diversity in devicetypes and time-of-day among UEs. Second, we study whether traditionalprobability distributions that have been widely adopted for modeling Internettraffic can model the control-plane traffic originated from individual UEs. Ouranalysis shows that the inter-arrival time of the control events as well as thesojourn time in the UE states of EMM and ECM for the cellular network cannot bemodeled as Poisson processes or other traditional probability distributions. Wefurther show that the reasons that these models fail to capture thecontrol-plane traffic are due to its higher burstiness and longer tails in thecumulative distribution than the traditional models. Third, we propose atwo-level hierarchical state-machine-based traffic model for UE clustersderived from our adaptive clustering scheme based on the Semi-Markov Model tocapture key characteristics of mobile network control-plane traffic -- inparticular, the dependence among events generated by each UE, and the diversityin device types and time-of-day among UEs. Finally, we show how our model canbe easily adjusted from LTE to 5G to support modeling 5G control-plane traffic,when the sizable control-plane trace for 5G UEs becomes available to train theadjusted model. The developed control-plane traffic generator for LTE/5Gnetworks is open-sourced to the research community to support high-performanceMCN architecture design R&D.