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.

LiTGen, a lightweight traffic generator: application to P2P and mail wireless traffic

Abstract: LiTGen is an easy to use and tune open-loop traffic generator that statistically models wireless traffic on a per user and application basis. We first show how to calibrate the underlying hierarchical model, from packet level capture originating in an ISP wireless network. Using wavelet and semi-experiments analysis, we then prove LiTGen's ability to reproduce accurately the captured traffic burstiness and internal properties over a wide range of timescales. In addition the flexibility of LiTGen enables us to investigate the sensitivity of the traffic structure with respect to the possible distributions of the random variables involved in the model. Finally this study helps understanding the traffic scaling behaviors and their corresponding internal structure.

Method and structure for vehicular traffic prediction with link interactions

Abstract: A method and structure for predicting traffic on a network, includes a receiver which receives data related to traffic on at least a portion of a network. A calculator calculates a traffic prediction for at least a part of the network, the traffic prediction being calculated by using a deviation from a historical traffic on the network.

Characterization of Substation Process Bus Network Delays

Abstract: This paper presents the characterization of network delays in an IEC61850 process bus substation area network, both through theoretical analysis and simulations. Several design targets were defined considering the recommendations of standards and good design practices: number of network hops, total network delay, probability of the delay being exceeded, link load, network topology and availability. An analytical delay estimation methodology is proposed, considering both the steady-state traffic and traffic resulting from a breaker failure event. A complete substation is taken as an example for characterizing the network delays, considering a star network topology. Simulations allow to obtain the cumulative distribution functions and percentile values of network delays. Results show a good agreement between the simulation and the analytical analysis. While the delay is best characterized statistically through simulation, finding the maximum network delay through simulations can be very time consuming, making the analytical analysis more suitable.

Dynamic Feature Analysis and Measurement for Large-Scale Network Traffic Monitoring

Abstract: Measuring and monitoring the changes of network traffic patterns in large-scale networks are crucial for effective network management. In this paper, we present a framework and method for detecting and measuring the dynamic changes of the pivotal traffic patterns. A bidirectional regional flow model is established to aggregate traffic packets and extract the traffic metrics and profiles. The characteristics of the regional flows are analyzed and interesting findings are obtained. A directed graph model is applied to describe the flow metrics and six flow features are extracted to capture the dynamic changes of the flow patterns. The measurements based on Renyi entropy are developed to quantitatively monitor these changes. The experimental results based on the actual network traffic data traces show that the method presented in this paper can capture the dynamic changes of pivotal traffic patterns effectively.

Predicting route utilization and non-redundant failures in network environments

Abstract: A network analysis module may obtain information including but not limited to network traffic and topology information for a network environment including multiple separate networks and physical connections between the networks. The module generates a network topology model including but not limited to extreme-case failure or break information according to the network topology information, and applies the historical network traffic information to the network topology model to predict future expected normal traffic load and extreme-case traffic load for each route over the physical connections between the networks. Output may include one or more reports for each route, each report indicating the historical and predicted traffic levels for both normal and extreme-case scenarios of a respective route.