2,496 research outputs found
Web User Session Characterization via Clustering Techniques
We focus on the identification and definition of "Web user-sessions", an aggregation of several TCP connections generated by the same source host on the basis of TCP connection opening time. The identification of a user session is non trivial; traditional approaches rely on threshold based mechanisms, which are very sensitive to the value assumed for the threshold and may be difficult to correctly set. By applying clustering techniques, we define a novel methodology to identify Web user-sessions without requiring an a priori definition of threshold values. We analyze the characteristics of user sessions extracted from real traces, studying the statistical properties of the identified sessions. From the study it emerges that Web user-sessions tend to be Poisson, but correlation may arise during periods of network/hosts anomalous functioning
VANET Connectivity Analysis
Vehicular Ad Hoc Networks (VANETs) are a peculiar subclass of mobile ad hoc
networks that raise a number of technical challenges, notably from the point of
view of their mobility models. In this paper, we provide a thorough analysis of
the connectivity of such networks by leveraging on well-known results of
percolation theory. By means of simulations, we study the influence of a number
of parameters, including vehicle density, proportion of equipped vehicles, and
radio communication range. We also study the influence of traffic lights and
roadside units. Our results provide insights on the behavior of connectivity.
We believe this paper to be a valuable framework to assess the feasibility and
performance of future applications relying on vehicular connectivity in urban
scenarios
Characterizing and Modeling Control-Plane Traffic for Mobile Core Network
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
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