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.

Method and system for enhancing performance of a physical network under real-time control using simulation of a reference model

Abstract: A system and method for network simulation and enhancement includes an experiment configuration engine that provides various proposed traffic and/or network models, and a simulator responsive to the proposed traffic and/or network models to execute a plurality of simulations for the network using parallel discrete event simulation, to determine an optimal network configuration based upon an objective function for enhancing an aspect of network performance. The traffic and/or network models may be based on monitored data from the network indicating a current network state and current network traffic. Reconfiguration instructions for the new network configuration may be conveyed from the simulator to the network, so as to effectuate ongoing, real-time enhancement of the network. The network model(s) may cover internal operational details of individual network devices (e.g., routers and/or switches) as well as operation of the network as a whole.

Traffic generation and performance evaluation for mesh-based NoCs

Abstract: The designer of a system on a chip (SoC) that connects IP cores through a network on chip (NoC) needs methods to support application performance evaluation. Two key aspects these methods have to address are the generation and evaluation of network traffic. Traffic generation allows injecting packets in the network according to application constraint specifications such as transmission rate and end-to-end latency. Performance evaluation helps in computing latency and throughput at network channels/interfaces, as well as to identify congestion and hot-spots. This paper reviews related works in traffic generation and performance evaluation for mesh topology NoCs, and proposes general methods for both aspects. Three parameters are used here to define traffic generation: packet spatial distribution, packet injection rate and packet size. Two types of methods to evaluate performance in NoCs are discussed: (i) external evaluation, a common strategy found in related works, where the network is considered as a black box and traffic results are obtained only from the external network interfaces; (ii) internal evaluation, where performance is computed in each network channel. The paper presents the result of experiments conducted in an 8times8 mesh network, varying the routing algorithms and the number of virtual channels. The main contribution of this work is the set of methods for internal NoC evaluation, which help designers to optimize the network under different traffic scenarios

Spatio-Temporal Graph Convolutional Networks: A Deep Learning Framework for Traffic Forecasting

Abstract: Timely accurate traffic forecast is crucial for urban traffic control and guidance. Due to the high nonlinearity and complexity of traffic flow, traditional methods cannot satisfy the requirements of mid-and-long term prediction tasks and often neglect spatial and temporal dependencies. In this paper, we propose a novel deep learning framework, Spatio-Temporal Graph Convolutional Networks (STGCN), to tackle the time series prediction problem in traffic domain. Instead of applying regular convolutional and recurrent units, we formulate the problem on graphs and build the model with complete convolutional structures, which enable much faster training speed with fewer parameters. Experiments show that our model STGCN effectively captures comprehensive spatio-temporal correlations through modeling multi-scale traffic networks and consistently outperforms state-of-the-art baselines on various real-world traffic datasets.

Mutiplexing and congestion control

Abstract: Methods, systems and devices for network congestion control exploit the inherent burstiness of network traffic, using a wave-based characterization of network traffic and corresponding multiplexing methods and approaches.

Metropolitan network traffic demand study

Abstract: A comprehensive metropolitan network traffic demand model has been developed and used to determine the network characteristics of a large metropolitan area. Network connectivity based on traffic demand analysis has been determined from which node and ring characteristics were gathered. The distributions of connection capacity as a function of connection length and traffic by application were also presented. These data can be used to determine future requirements of fiber and equipment used in a large metropolitan network.