scholarly journals Traffic Trace Artifacts due to Monitoring Via Port Mirroring

Author(s):  
Jian Zhang ◽  
Andrew Moore
Keyword(s):  
2015 ◽  
Vol 2015 ◽  
pp. 1-19 ◽  
Author(s):  
Zongjian He ◽  
Buyang Cao ◽  
Yan Liu

Real-time traffic speed is indispensable for many ITS applications, such as traffic-aware route planning and eco-driving advisory system. Existing traffic speed estimation solutions assume vehicles travel along roads using constant speed. However, this assumption does not hold due to traffic dynamicity and can potentially lead to inaccurate estimation in real world. In this paper, we propose a novel in-network traffic speed estimation approach using infrastructure-free vehicular networks. The proposed solution utilizes macroscopic traffic flow model to estimate the traffic condition. The selected model only relies on vehicle density, which is less likely to be affected by the traffic dynamicity. In addition, we also demonstrate an application of the proposed solution in real-time route planning applications. Extensive evaluations using both traffic trace based large scale simulation and testbed based implementation have been performed. The results show that our solution outperforms some existing ones in terms of accuracy and efficiency in traffic-aware route planning applications.


2021 ◽  
Author(s):  
Ginno Millán

This paper presents a simple and fast technique of multifractal traffic modeling. It proposes a method of fitting model to a given traffic trace. A comparison of simulation results obtained for an exemplary trace, multifractal model and Markov Modulated Poisson Process models has been performed.


Author(s):  
Ginno Millán

This paper presents a simple and fast technique of multifractal traffic modeling. It proposes a method of fitting model to a given traffic trace. A comparison of simulation results obtained for an exemplary trace, multifractal model and Markov Modulated Poisson Process models has been performed.


2008 ◽  
Vol 15 (2) ◽  
pp. 87-104
Author(s):  
Flávio Henrique Teles Vieira ◽  
Lee Luan Ling

In this paper we propose a multifractal traffic model that is based on a multiplicative cascade presenting specific multiplier distributions in each cascade stage. In the proposed model, the multipliers are obtained through the estimate of their probability densities found in real network traffic by using Kernel and Acceptance/Rejection methods. Statistical analysis and queueing behavior study were carried out for the model validation. Furthermore, we verify the model performance in capturing the traffic trace characteristics in comparison to other multifractal models.


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