8,608 research outputs found
Design and performance evaluation of a state-space based AQM
Recent research has shown the link between congestion control in
communication networks and feedback control system. In this paper, the design
of an active queue management (AQM) which can be viewed as a controller, is
considered. Based on a state space representation of a linearized fluid flow
model of TCP, the AQM design is converted to a state feedback synthesis problem
for time delay systems. Finally, an example extracted from the literature and
simulations via a network simulator NS (under cross traffic conditions) support
our study
Data-driven design of intelligent wireless networks: an overview and tutorial
Data science or "data-driven research" is a research approach that uses real-life data to gain insight about the behavior of systems. It enables the analysis of small, simple as well as large and more complex systems in order to assess whether they function according to the intended design and as seen in simulation. Data science approaches have been successfully applied to analyze networked interactions in several research areas such as large-scale social networks, advanced business and healthcare processes. Wireless networks can exhibit unpredictable interactions between algorithms from multiple protocol layers, interactions between multiple devices, and hardware specific influences. These interactions can lead to a difference between real-world functioning and design time functioning. Data science methods can help to detect the actual behavior and possibly help to correct it. Data science is increasingly used in wireless research. To support data-driven research in wireless networks, this paper illustrates the step-by-step methodology that has to be applied to extract knowledge from raw data traces. To this end, the paper (i) clarifies when, why and how to use data science in wireless network research; (ii) provides a generic framework for applying data science in wireless networks; (iii) gives an overview of existing research papers that utilized data science approaches in wireless networks; (iv) illustrates the overall knowledge discovery process through an extensive example in which device types are identified based on their traffic patterns; (v) provides the reader the necessary datasets and scripts to go through the tutorial steps themselves
Detection and localization of change-points in high-dimensional network traffic data
We propose a novel and efficient method, that we shall call TopRank in the
following paper, for detecting change-points in high-dimensional data. This
issue is of growing concern to the network security community since network
anomalies such as Denial of Service (DoS) attacks lead to changes in Internet
traffic. Our method consists of a data reduction stage based on record
filtering, followed by a nonparametric change-point detection test based on
-statistics. Using this approach, we can address massive data streams and
perform anomaly detection and localization on the fly. We show how it applies
to some real Internet traffic provided by France-T\'el\'ecom (a French Internet
service provider) in the framework of the ANR-RNRT OSCAR project. This approach
is very attractive since it benefits from a low computational load and is able
to detect and localize several types of network anomalies. We also assess the
performance of the TopRank algorithm using synthetic data and compare it with
alternative approaches based on random aggregation.Comment: Published in at http://dx.doi.org/10.1214/08-AOAS232 the Annals of
Applied Statistics (http://www.imstat.org/aoas/) by the Institute of
Mathematical Statistics (http://www.imstat.org
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