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Tmix: a tool for generating realistic TCP application workloads in ns-2

Authors:

Michele C. Weigle,

Prashanth Adurthi,

Félix Hernández-Campos,

F. Donelson SmithAuthors Info & Claims

ACM SIGCOMM Computer Communication Review, Volume 36, Issue 3

Pages 65 - 76

https://doi.org/10.1145/1140086.1140094

Published: 05 July 2006 Publication History

Abstract

In order to perform realistic network simulations, one needs a traffic generator that is capable of generating realistic synthetic traffic in a closed-loop fashion that "looks like" traffic found on an actual network. We describe such a traffic generation system for the widely used ns-2 simulator. The system takes as input a packet header trace taken from a network link of interest. The trace is "reverse compiled" into a source-level characterization of each TCP connection present in the trace. The characterization, called a connection vector, is then used as input to an ns module called tmix that emulates the socket-level behavior of the source application that created the corresponding connection in the trace. This emulation faithfully reproduces the essential pattern of socket reads and writes that the original application performed without knowledge of what the original application actually was. When combined with a network path emulation component we have constructed called DelayBox, the resulting traffic generated in the simulation is statistically representative of the traffic measured on the real link. This approach to synthetic traffic generation allows one to automatically repro-duce in ns the full range of TCP connections found on an arbitrary link. Thus with our tools, researchers no longer need make arbitrary decisions on how traffic is generated in simulations and can instead easily generate TCP traffic that represents the use of a net-work by the full mix of applications measured on actual network links of interest. The method is evaluated by applying it to packet header traces taken from campus and wide-area networks and comparing the statistical properties of traffic on the measured links with traffic generated by tmix in ns.

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Cited By

Qi XChai HYu LLi YWang Z(2024)Regional Features Conditioned Diffusion Models for 5G Network Traffic GenerationProceedings of the 32nd ACM International Conference on Advances in Geographic Information Systems10.1145/3678717.3691312(396-409)Online publication date: 29-Oct-2024
https://dl.acm.org/doi/10.1145/3678717.3691312
Li THui SZhang SWang HZhang YHui PJin DLi Y(2024)Mobile User Traffic Generation Via Multi-Scale Hierarchical GANACM Transactions on Knowledge Discovery from Data10.1145/366465518:8(1-19)Online publication date: 10-May-2024
https://dl.acm.org/doi/10.1145/3664655
Kong ZHu NHu YMeng JKoral YVallina-Rodríguez NSuarez-Tángil GLevin DPelsser C(2024)High-Fidelity Cellular Network Control-Plane Traffic Generation without Domain KnowledgeProceedings of the 2024 ACM on Internet Measurement Conference10.1145/3646547.3688422(530-544)Online publication date: 4-Nov-2024
https://dl.acm.org/doi/10.1145/3646547.3688422
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Index Terms

Tmix: a tool for generating realistic TCP application workloads in ns-2
1. Computing methodologies
  1. Modeling and simulation
    1. Model development and analysis
      1. Modeling methodologies
2. Networks
  1. Network protocols
  2. Network services

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Information & Contributors

Information

Published In

cover image ACM SIGCOMM Computer Communication Review

ACM SIGCOMM Computer Communication Review Volume 36, Issue 3

July 2006

97 pages

ISSN:0146-4833

DOI:10.1145/1140086

Issue’s Table of Contents

Copyright © 2006 Authors.

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 05 July 2006

Published in SIGCOMM-CCR Volume 36, Issue 3

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Cited By

Qi XChai HYu LLi YWang Z(2024)Regional Features Conditioned Diffusion Models for 5G Network Traffic GenerationProceedings of the 32nd ACM International Conference on Advances in Geographic Information Systems10.1145/3678717.3691312(396-409)Online publication date: 29-Oct-2024
https://dl.acm.org/doi/10.1145/3678717.3691312
Li THui SZhang SWang HZhang YHui PJin DLi Y(2024)Mobile User Traffic Generation Via Multi-Scale Hierarchical GANACM Transactions on Knowledge Discovery from Data10.1145/366465518:8(1-19)Online publication date: 10-May-2024
https://dl.acm.org/doi/10.1145/3664655
Kong ZHu NHu YMeng JKoral YVallina-Rodríguez NSuarez-Tángil GLevin DPelsser C(2024)High-Fidelity Cellular Network Control-Plane Traffic Generation without Domain KnowledgeProceedings of the 2024 ACM on Internet Measurement Conference10.1145/3646547.3688422(530-544)Online publication date: 4-Nov-2024
https://dl.acm.org/doi/10.1145/3646547.3688422
Hui SWang HLi TYang XWang XFeng JZhu LDeng CHui PJin DLi YSingh ASun YAkoglu LGunopulos DYan XKumar ROzcan FYe J(2023)Large-scale Urban Cellular Traffic Generation via Knowledge-Enhanced GANs with Multi-Periodic PatternsProceedings of the 29th ACM SIGKDD Conference on Knowledge Discovery and Data Mining10.1145/3580305.3599853(4195-4206)Online publication date: 6-Aug-2023
https://dl.acm.org/doi/10.1145/3580305.3599853
Zhang SLi THui SLi GLiang YYu LJin DLi YSingh ASun YAkoglu LGunopulos DYan XKumar ROzcan FYe J(2023)Deep Transfer Learning for City-scale Cellular Traffic Generation through Urban Knowledge GraphProceedings of the 29th ACM SIGKDD Conference on Knowledge Discovery and Data Mining10.1145/3580305.3599801(4842-4851)Online publication date: 6-Aug-2023
https://dl.acm.org/doi/10.1145/3580305.3599801
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Le FSrivatsa MGanti RSekar V(2022)Rethinking data-driven networking with foundation modelsProceedings of the 21st ACM Workshop on Hot Topics in Networks10.1145/3563766.3564109(188-197)Online publication date: 14-Nov-2022
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DeAngelis DHussain AKocoloski BArdi CSchwab S(2022)Generating Representative Video Teleconferencing TrafficProceedings of the 15th Workshop on Cyber Security Experimentation and Test10.1145/3546096.3546107(100-104)Online publication date: 8-Aug-2022
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Yin YLin ZJin MFanti GSekar VKuipers FOrda A(2022)Practical GAN-based synthetic IP header trace generation using NetShareProceedings of the ACM SIGCOMM 2022 Conference10.1145/3544216.3544251(458-472)Online publication date: 22-Aug-2022
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