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.

Impact of utilizing forecasted network traffic for data transfers

Abstract: Sharing of information leads to the need to transfer data between geographically distant locations. Identifying the most appropriate time period to execute the data transfer is essential to achieve the best data transfer throughput; e.g. one can forecast network traffic, identify future low network traffic activities between two entities, and plan the data transfer accordingly. This forecasting can be done using Autoregressive Moving Average (ARMA) time series model. In this paper, we conduct an empirical study in a controlled laboratory environment to realise the impact of performing data transfer during the forecasted low network traffic activities. The network information is captured using a network analyzer and post-processed to create stationary data. This data is then passed to ARMA and the forecasting results produced by ARMA is post-processed to derive the forecasted network traffic activities. Comparison is made between the throughputs of the data transfers initiated when the forecasted network traffic is low and when the forecasted network traffic is high.

Fuzzy Neural Network Model Applied in the Traffic Flow Prediction

Abstract: The paper proposes a fuzzy neural network model(FNNM) strategy for predicting the traffic flow of real time traffic control systems. The proposed model is composed of two modular. One is a fuzzy network (FN), which is used for fuzzy clustering. Each cluster represents one kind of specific traffic pattern. The other is a neural network (NN), which is one-layer network and is used for partitioning the relationship of input and output vector. And the FN module supervises the learning of the NN. That is, the features of the traffic samples are employed to guide the training of the NN. Moreover, an on-line iterative predictive algorithm is presented in this paper to predict the traffic flow according to the sampled data of the upstream cross roads. Finally, the real sampled traffic flow data is employed to validate the proposed method. Results show that the proposed traffic flow prediction strategy based on fuzzy neural network model is feasible and effective.

Short-Term Traffic Flow Prediction Based on RBF Neural Network

Abstract: According to the characteristics of short-term traffic flow, this paper applies the neural network architectures and sets up the prediction model of RBF neural network. At last, the model is verified by real short-term traffic volume of a freeway.

Multilevel Monitoring System Architecture

Abstract: Network traffic information from multiple sources, at multiple time scales, and at multiple levels of detail are integrated so that users may more easily identify relevant network information. The network monitoring system stores and manipulates low-level and higher-level network traffic data separately to enable efficient data collection and storage. Packet traffic data is collected, stored, and analyzed at multiple locations. The network monitoring locations communicate summary and aggregate data to central modules, which combine this data to provide an end-to-end description of network traffic at coarser time scales. The network monitoring system enables users to zoom in on high-level, coarse time scale network performance data to one or more lower levels of network performance data at finer time scales. When high-level network performance data of interest is selected, corresponding low-level network performance data is retrieved from the appropriate distributed network monitoring locations to provide additional detailed information.

An engineering approach to dynamic prediction of network performance from application logs

Abstract: Network measurement traces contain information regarding network behavior over the period of observation. Research carried out from different contexts shows predictions of network behavior can be made depending on network past history. Existing works on network performance prediction use a complicated stochastic modeling approach that extrapolates past data to yield a rough estimate of long-term future network performance. However, prediction of network performance in the immediate future is still an unresolved problem. In this paper, we address network performance prediction as an engineering problem. The main contribution of this paper is to predict network performance dynamically for the immediate future. Our proposal also considers the practical implication of prediction. Therefore, instead of following the conventional approach to predict one single value, we predict a range within which network performance may lie. This range is bounded by our two newly proposed indices, namely, Optimistic Network Performance Index (ONPI) and Robust Network Performance Index (RNPI). Experiments carried out using one-year-long traffic traces between several pairs of real-life networks validate the usefulness of our model.