85,396 research outputs found
A critical look at power law modelling of the Internet
This paper takes a critical look at the usefulness of power law models of the
Internet. The twin focuses of the paper are Internet traffic and topology
generation. The aim of the paper is twofold. Firstly it summarises the state of
the art in power law modelling particularly giving attention to existing open
research questions. Secondly it provides insight into the failings of such
models and where progress needs to be made for power law research to feed
through to actual improvements in network performance.Comment: To appear Computer Communication
Traffic measurement and analysis
Measurement and analysis of real traffic is important to gain knowledge
about the characteristics of the traffic. Without measurement, it is
impossible to build realistic traffic models. It is recent that data
traffic was found to have self-similar properties. In this thesis work
traffic captured on the network at SICS and on the Supernet, is shown to
have this fractal-like behaviour. The traffic is also examined with
respect to which protocols and packet sizes are present and in what
proportions. In the SICS trace most packets are small, TCP is shown to be
the predominant transport protocol and NNTP the most common application.
In contrast to this, large UDP packets sent between not well-known ports
dominates the Supernet traffic. Finally, characteristics of the client
side of the WWW traffic are examined more closely. In order to extract
useful information from the packet trace, web browsers use of TCP and HTTP
is investigated including new features in HTTP/1.1 such as persistent
connections and pipelining. Empirical probability distributions are
derived describing session lengths, time between user clicks and the
amount of data transferred due to a single user click. These probability
distributions make up a simple model of WWW-sessions
Characteristics of WAP traffic
This paper considers the characteristics of Wireless Application Protocol (WAP) traffic. We start by constructing a WAP traffic model by analysing the behaviour of users accessing public WAP sites via a monitoring system. A wide range of different traffic scenarios were considered, but most of these scenarios resolve to one of two basic types. The paper then uses this traffic model to consider the effects of large quantities of WAP traffic on the core network. One traffic characteristic which is of particular interest in network dimensioning is the degree of self-similarity, so the paper looks at the characteristics of aggregated traffic with WAP, Web and packet speech components to estimate its self-similarity. The results indicate that, while WAP traffic alone does not exhibit a significant degree of self-similarity, a combined load from various traffic sources retains almost the same degree of self-similarity as the most self-similar individual source
Vision-Based Lane-Changing Behavior Detection Using Deep Residual Neural Network
Accurate lane localization and lane change detection are crucial in advanced
driver assistance systems and autonomous driving systems for safer and more
efficient trajectory planning. Conventional localization devices such as Global
Positioning System only provide road-level resolution for car navigation, which
is incompetent to assist in lane-level decision making. The state of art
technique for lane localization is to use Light Detection and Ranging sensors
to correct the global localization error and achieve centimeter-level accuracy,
but the real-time implementation and popularization for LiDAR is still limited
by its computational burden and current cost. As a cost-effective alternative,
vision-based lane change detection has been highly regarded for affordable
autonomous vehicles to support lane-level localization. A deep learning-based
computer vision system is developed to detect the lane change behavior using
the images captured by a front-view camera mounted on the vehicle and data from
the inertial measurement unit for highway driving. Testing results on
real-world driving data have shown that the proposed method is robust with
real-time working ability and could achieve around 87% lane change detection
accuracy. Compared to the average human reaction to visual stimuli, the
proposed computer vision system works 9 times faster, which makes it capable of
helping make life-saving decisions in time
The State-of-the-art of Coordinated Ramp Control with Mixed Traffic Conditions
Ramp metering, a traditional traffic control strategy for conventional
vehicles, has been widely deployed around the world since the 1960s. On the
other hand, the last decade has witnessed significant advances in connected and
automated vehicle (CAV) technology and its great potential for improving
safety, mobility and environmental sustainability. Therefore, a large amount of
research has been conducted on cooperative ramp merging for CAVs only. However,
it is expected that the phase of mixed traffic, namely the coexistence of both
human-driven vehicles and CAVs, would last for a long time. Since there is
little research on the system-wide ramp control with mixed traffic conditions,
the paper aims to close this gap by proposing an innovative system architecture
and reviewing the state-of-the-art studies on the key components of the
proposed system. These components include traffic state estimation, ramp
metering, driving behavior modeling, and coordination of CAVs. All reviewed
literature plot an extensive landscape for the proposed system-wide coordinated
ramp control with mixed traffic conditions.Comment: 8 pages, 1 figure, IEEE INTELLIGENT TRANSPORTATION SYSTEMS CONFERENCE
- ITSC 201
MR-BART: Multi-Rate Available Bandwidth Estimation in Real-Time
In this paper, we propose Multi-Rate Bandwidth Available in Real Time
(MR-BART) to estimate the end-to-end Available Bandwidth (AB) of a network
path. The proposed scheme is an extension of the Bandwidth Available in Real
Time (BART) which employs multi-rate (MR) probe packet sequences with Kalman
filtering. Comparing to BART, we show that the proposed method is more robust
and converges faster than that of BART and achieves a more AB accurate
estimation. Furthermore, we analyze the estimation error in MR-BART and obtain
analytical formula and empirical expression for the AB estimation error based
on the system parameters.Comment: 12 Pages (Two columns), 14 Figures, 4 Tables
A Long Short-Term Memory Recurrent Neural Network Framework for Network Traffic Matrix Prediction
Network Traffic Matrix (TM) prediction is defined as the problem of
estimating future network traffic from the previous and achieved network
traffic data. It is widely used in network planning, resource management and
network security. Long Short-Term Memory (LSTM) is a specific recurrent neural
network (RNN) architecture that is well-suited to learn from experience to
classify, process and predict time series with time lags of unknown size. LSTMs
have been shown to model temporal sequences and their long-range dependencies
more accurately than conventional RNNs. In this paper, we propose a LSTM RNN
framework for predicting short and long term Traffic Matrix (TM) in large
networks. By validating our framework on real-world data from GEANT network, we
show that our LSTM models converge quickly and give state of the art TM
prediction performance for relatively small sized models.Comment: Submitted for peer review. arXiv admin note: text overlap with
arXiv:1402.1128 by other author
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