Detection of stationary network load increase using univariate network aggregate traffic data by dynamic PCA

2011 ◽  
Author(s):  
Shengkun Xie ◽  
Anna T. Lawniczak
Keyword(s):  
Traffic Data ◽  
Network Load ◽  
Load Increase

scholarly journals Study of Stationary Load Increase of Computer-Network Traffic via Dynamic Principal-Component Analysis

2012 ◽  
Vol 2012 ◽  
pp. 1-10
Author(s):  
Shengkun Xie ◽  
Anna T. Lawniczak
Keyword(s):  
Principal Component Analysis ◽  
Network Traffic ◽  
Performance Indicator ◽  
Principal Component ◽  
Component Analysis ◽  
Switching Network ◽  
Traffic Data ◽  
Load Increase ◽  
Dynamic Principal Component Analysis ◽  
Detection Schemes

Many network monitoring applications and performance analysis tools are based on the study of an aggregate measure of network traffic, for example, number of packets in transit (NPT). The simulation modeling and analysis of this type of performance indicator enables a theoretical investigation of the underlying complex system through different combination of network setups such as routing algorithms, network source loads or network topologies. To detect stationary increase of network source load, we propose a dynamic principal component analysis (PCA) method, first to extract data features and then to detect a stationary load increase. The proposed detection schemes are based on either the major or the minor principal components of network traffic data. To demonstrate the applications of the proposed method, we first applied them to some synthetic data and then to network traffic data simulated from the packet switching network (PSN) model. The proposed detection schemes, based on dynamic PCA, show enhanced performance in detecting an increase of network load for the simulated network traffic data. These results show usefulness of a new feature extraction method based on dynamic PCA that creates additional feature variables for event detection in a univariate time series.

Privacy: Users of Pharmaceutical Websites

2002 ◽  
Vol 30 (3) ◽  
pp. 466-474
Keyword(s):  
Personal Information ◽  
District Court ◽  
Traffic Data ◽  
Web Traffic ◽  
Electronic Communications ◽  
Private Action

In In re Pharmatrak, Inc. Privacy Litigation, website users brought suit claiming that major pharmaceutical corporations and a web monitoring company violated three federal statutes protecting electronic communications and data by collecting web traffic data and personal information about website users. On August 13,2002, the District Court of Massachusetts dismissed these allegations, holding that the defendants were parties to the communications and thus exempted under the statutory language.The court also found that plaintiffs had not suffered an amount of damages required to sustain private action.

Effect of Field Setup on Accuracy of Traffic Data Collection Using Machine Vision Technology

2002 ◽  
Vol 1804 (1) ◽  
pp. 62-66 ◽  
Author(s):  
Sunita Nadella ◽  
Lloyd A. Herman
Keyword(s):  
Machine Vision ◽  
Focal Length ◽  
Reference Points ◽  
Specific Reference ◽  
Traffic Data ◽  
Video Traffic ◽  
Vertical Angle ◽  
Traffic Data Collection ◽  
Camera Position ◽  
Parameter Ranges

Video traffic data were collected in 24 combinations of four different camera position parameters. A machine vision processor was used to detect vehicle speeds and volumes from the videotapes. The machine vision results were then compared with the actual vehicle volumes and speeds to give the percentage errors in each case. The results of the study provide a procedure with which to establish camera position parameters with specific reference points to help machine vision users select suitable camera positions and develop appropriate measurement error expectations. The camera position parameters that were most likely to produce the least overall volume and speed errors, for the specific site and field setup with the parameter ranges used in this study, were the low height of approximately 7.6 m (25 ft), with an upstream orientation (traffic moving toward the camera), a 50-mm (midangle) focal length, and a 15° vertical angle.

Cloud computing demand elasticity algorithm based on ant colony algorithm

2020 ◽  
Vol 13 ◽  
Author(s):  
Chunyu Liu ◽  
Fengrui Mu ◽  
Weilong Zhang
Keyword(s):  
Ant Colony Algorithm ◽  
Theory Of Elasticity ◽  
Ant Colony ◽  
Demand Elasticity ◽  
Network Efficiency ◽  
Elasticity Of Demand ◽  
Operating Profit ◽  
Network Load ◽  
Network Utilization ◽  
The Impact

Background: In recent era of technology, the traditional Ant Colony Algorithm (ACO) is insufficient in solving the problem of network congestion and load balance, and network utilization. Methods: This paper proposes an improved ant colony algorithm, which considers the price factor based on the theory of elasticity of demand. The price factor is denominated in the impact on the network load which means indirect control of network load, congestion or auxiliary solution to calculate the idle resources caused by the low network utilization and reduced profits. Results: Experimental results show that the improved algorithm can balance the overall network load, extend the life of path by nearly 3 hours, greatly reduce the risk of network paralysis, and increase the profit of the manufacturer by 300 million Yuan. Conclusion: Furthermore, results shows that the improved method has a great application value in improving the network efficiency, balancing network load, prolonging network life and increasing network operating profit.

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