Abstract
Nowadays, the traffic over the networks is changing because of new protocols, devices and applications. Therefore, it is necessary to analyze the impact over services and resources. Traffic Classification of network is a very important prerequisite for tasks such as traffic engineering and provisioning quality of service. In this paper, we analyze the variable packet size of the traffic in an university campus network through the collected data using a novel sniffer that ensures the user data privacy. We separate the collected data by type of traffic, protocols and applications. Finally, we estimate the traffic model that represents this traffic by means of a Poisson process and compute its associated numerical parameters.
Highlights
IntroductionUnderstanding the behavior of the network traffic is crucial and an important prerequisite for planning the traffic engineering and apply quality of service; for traffic modeling and prediction
Understanding the behavior of the network traffic is crucial and an important prerequisite for planning the traffic engineering and apply quality of service; for traffic modeling and prediction.the network traffic is changing because of the convergence
New protocols like the IPv6 are present in the internet, and technologies such as Internet of Things (IoT) will allow the connection of millions of new devices
Summary
Understanding the behavior of the network traffic is crucial and an important prerequisite for planning the traffic engineering and apply quality of service; for traffic modeling and prediction. When we need analyze and modelling the network traffic, we can to considerate two stochastically variables: the packet size and the inter-arrival time [3]. The active method generates new traffic, inject it into the network, while passive method consists on monitor, and capture the network traffic. In this case, we use the passive form for capture traffic, analyze the packet headers and produce statistics. The rest of the paper is organized as follows: section 2 provides information about related works; in section 3 we present the novel sniffer; in section 4 we show the data collection, classified by type of traffic, by protocols, and by application, according to the variable packet size.
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