Network traffic simulation

About: Network traffic simulation is a research topic. Over the lifetime, 4535 publications have been published within this topic receiving 74606 citations.

An SVM-based machine learning method for accurate internet traffic classification

Abstract: Accurate and timely traffic classification is critical in network security monitoring and traffic engineering. Traditional methods based on port numbers and protocols have proven to be ineffective in terms of dynamic port allocation and packet encapsulation. The signature matching methods, on the other hand, require a known signature set and processing of packet payload, can only handle the signatures of a limited number of IP packets in real-time. A machine learning method based on SVM (supporting vector machine) is proposed in this paper for accurate Internet traffic classification. The method classifies the Internet traffic into broad application categories according to the network flow parameters obtained from the packet headers. An optimized feature set is obtained via multiple classifier selection methods. Experimental results using traffic from campus backbone show that an accuracy of 99.42% is achieved with the regular biased training and testing samples. An accuracy of 97.17% is achieved when un-biased training and testing samples are used with the same feature set. Furthermore, as all the feature parameters are computable from the packet headers, the proposed method is also applicable to encrypted network traffic.

Network game traffic modelling

Abstract: A significant share of today's Internet traffic is generated by network gaming. This kind of traffic is interesting in regard to it's market potential as well as to it's real time requirements on the network. For the consideration of game traffic in network dimensioning, traffic models are required that allow to generate a characteristic load for analytical or simulative performance evaluation of networks. In this paper we evaluate the fast action multiplayer game ,,Counter Strike" from a 36 hour LAN party measurement and present traffic models for client and server. The paper concludes with remarks on the use of game traffic models in simulations and on QoS metrics for an adequate evaluation of simulation results.

Spatial modeling of the traffic density in cellular networks

Abstract: Modeling and simulation of a cellular network typically assumes that the target area is divided into regular hexagonal cells and mobile stations (MSs) are uniformly scattered in each cell. This implies a statistically uniform distribution of traffic load over space, but in reality the spatial traffic distribution is highly non-uniform across different cells, which calls for actual spatial traffic models. In this article, we first present the analysis of traffic measurements collected from commercial cellular networks in China, and demonstrate that the spatial distribution of the traffic density (the traffic load per unit area) can be approximated by the log-normal or Weibull distribution depending on time and space. Then we propose a spatial traffic model which generates large-scale spatial traffic variations by a sum of sinusoids that captures the characteristics of log-normally distributed and spatially correlated cellular traffic. The proposed model can be directly used to generate realistic spatial traffic patterns for cellular network simulations, such as performance evaluations of network planning and load balancing.

Provisioning IP backbone networks to support latency sensitive traffic

Abstract: To support latency sensitive traffic such as voice, network providers can either use service differentiation to prioritize such traffic or provision their network with enough bandwidth so that all traffic meets the most stringent delay requirements. In the context of wide-area Internet backbones, two factors make overprovisioning an attractive approach. First, the high link speeds and large volumes of traffic make service differentiation complex and potentially costly to deploy. Second, given the degree of aggregation and resulting traffic characteristics, the amount of overprovisioning necessary may not be very large. This study develops a methodology to compute the amount of overprovisioning required to support a given delay requirement. We first develop a model for backbone traffic which is needed to compute the end-to-end delay through the network. The model is validated using 331 one-hour traffic measurements collected from the Sprint IP network. We then develop a procedure which uses this model to find the amount of bandwidth needed on each link in the network so that an end-to-end delay requirement is satisfied. Applying this procedure to the Sprint network, we find that satisfying end-to-end delay requirements as low as 3 ms requires only 15% extra bandwidth above the average data rate of the traffic.