Swing Realistic And Responsive Network Traffic Generation Pdf

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Obtaining data for the training of failure prediction algorithms has long been an issue. A framework for automating the generation of this data for the training and deployment of these algorithms has recently been introduced. Unfortunately, the framework was only tested on a single deprecated operating system.

4. Internet Traffic Profiling

This paper presents Swing, a closed-loop, network-responsive traffic generator that accurately captures the packet interactions of a range of applications using a simple structural model. Starting from observed traffic at a single point in the network, Swing automatically extracts distributions for user, application, and network behavior. It then generates live traffic corresponding to the underlying models in a network emulation environment running commodity network protocol stacks.

We find that the generated traces are statistically similar to the original traces. Further, to the best of our knowledge, we are the first to reproduce burstiness in traffic across a range of timescales using a model applicable to a variety of network settings. An initial sensitivity analysis reveals the importance of capturing and recreating user, application, and network characteristics to accurately reproduce such burstiness.

Documents: Advanced Search Include Citations. Authors: Advanced Search Include Citations. Abstract This paper presents Swing, a closed-loop, network-responsive traffic generator that accurately captures the packet interactions of a range of applications using a simple structural model. Powered by:.

Internet Traffic Profiling

Skip to search form Skip to main content You are currently offline. Some features of the site may not work correctly. DOI: Vishwanath and A. Vishwanath , A. This paper presents Swing, a closed-loop, network-responsive traffic generator that accurately captures the packet interactions of a range of applications using a simple structural model. Starting from observed traffic at a single point in the network, Swing automatically extracts distributions for user, application, and network behavior.

Generating Realistic Environments for Cyber Operations Development, Testing, and Training

This paper presents Swing, a closed-loop, network-responsive traffic generator that accurately captures the packet interactions of a range of applications using a simple structural model. Starting from observed traffic at a single point in the network, Swing automatically extracts distributions for user, application, and network behavior. It then generates live traffic corresponding to the underlying models in a network emulation environment running commodity network protocol stacks. We find that the generated traces are statistically similar to the original traces.

Skip to Main Content. A not-for-profit organization, IEEE is the world's largest technical professional organization dedicated to advancing technology for the benefit of humanity. Use of this web site signifies your agreement to the terms and conditions. Swing: Realistic and Responsive Network Traffic Generation Abstract: This paper presents Swing, a closed-loop, network-responsive traffic generator that accurately captures the packet interactions of a range of applications using a simple structural model. Starting from observed traffic at a single point in the network, Swing automatically extracts distributions for user, application, and network behavior.

Experimental networking has evolved significantly over the last two decades, but it remains a daunting endeavor. Throughout this time, traffic generation, a key component for experimental networking, has remained a major challenge. What is traffic generation and what role does it play in empirical networking research?

An end-to-end network traffic matrix is significantly helpful for network management and for Software Defined Networks SDN. However, the end-to-end network traffic matrix's inferences and estimations are a challenging problem. Moreover, attaining the traffic matrix in high-speed networks for SDN is a prohibitive challenge.

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Network neutrality on the internet: A two-sided market analysis. Information Economics and Policy 24 2 : 91— Network neutrality and congestion sensitive content providers: Implications for content variety, broadband investment, and regulation. Information Systems Research 23 4 : — ACM, Glasnost: Enabling end users to detect traffic differentiation.

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