  In this paper, we first carry out to our knowledge the first in-depth
characterization of control-plane traffic, using a real-world control-plane
trace for 37,325 UEs sampled at a real-world LTE Mobile Core Network (MCN). Our
analysis shows that control events exhibit significant diversity in device
types and time-of-day among UEs. Second, we study whether traditional
probability distributions that have been widely adopted for modeling Internet
traffic can model the control-plane traffic originated from individual UEs. Our
analysis shows that the inter-arrival time of the control events as well as the
sojourn time in the UE states of EMM and ECM for the cellular network cannot be
modeled as Poisson processes or other traditional probability distributions. We
further show that the reasons that these models fail to capture the
control-plane traffic are due to its higher burstiness and longer tails in the
cumulative distribution than the traditional models. Third, we propose a
two-level hierarchical state-machine-based traffic model for UE clusters
derived from our adaptive clustering scheme based on the Semi-Markov Model to
capture key characteristics of mobile network control-plane traffic -- in
particular, the dependence among events generated by each UE, and the diversity
in device types and time-of-day among UEs. Finally, we show how our model can
be 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 the
adjusted model. The developed control-plane traffic generator for LTE/5G
networks is open-sourced to the research community to support high-performance
MCN architecture design R&D.
