Abstract
It has become clear that the traditional Poisson model of data network traffic is insufficient for dimensioning and analyzing the performance of real-life networks.Fractal models are more appropriate for simulating the self-similar behavior of data traffic.To understand self-similarity on physical grounds in a realistic network environment is important when developing efficient and integrated network frameworks within which end-to-end QoS guarantees are fully supported. OPNET features the Raw Packet Generator (RPG) which contains several implementations of self-similar sources. This paper uses fractal analysis to characterize increasingly bursty industrial control network traffic.The goal is to develop a better understanding of the fractal nature of network traffic, which in turn will lead to more efficiency and better quality of services on industrial control network traffic. We present a comparison between the different RPG models in OPNET Modeler.
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Zhou, Sx., Han, Jh., Tang, H. (2011). Fractal Traffic Analysis and Applications in Industrial Control Ethernet Network. In: Deng, H., Miao, D., Wang, F.L., Lei, J. (eds) Emerging Research in Artificial Intelligence and Computational Intelligence. AICI 2011. Communications in Computer and Information Science, vol 237. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-24282-3_6
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DOI: https://doi.org/10.1007/978-3-642-24282-3_6
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-24281-6
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