Traffic Generators for Internet Traffic
web page on traffic generators.
web page on traffic generators.
Polly Huang's traffic generator in NS:
Anja Feldmann, Anna C. Gilbert,
Polly Huang and Walter Willinger.
Dynamics of IP Traffic: A Study of the
Role of Variability and the Impact of Control, SIGCOMM'99, Boston, MA,
"During a Web session, a user usually requests several Web pages and each
page may contain several web objects. To capture this hierarchical
structure and its inherent variability, we allow for different
probability distributions for the following user/session
attributes: inter-session time, pages per session, inter-page time,
objects per page, inter-object time, and object size... We base our
choice of distributions on the work surrounding SURGE... and upon [Feldman99,
NSWEB traffic generator for NS-2.29.
The PackMime-HTTP traffic generator in NS:
J. Cao, W.S. Cleveland, Y. Gao, K. Jeffay, F.D. Smith, and M.C. Weigle,
Stochastic Models for Generating Synthetic HTTP Source Traffic,
IEEE INFOCOM, March 2004.
``New source-level models for aggregated HTTP traffic ... are built and
validated using two large-scale collections of TCP/IP packet header
traces." The TCP connections are modeled ``in terms of connection
establishment rates and the sizes and timing of exchanges of request
and response data."
The PackMime-HTTP traffic generator requires the use of Full-TCP in NS.
The tmix traffic generator.
M. Weigle, P. Adurthi, F. Hernandez-Campos,
K. Jeffay and F. D. Smith,
Tmix: A Tool for Generating Realistic TCP Application Workloads in
CCR, July 2006.
"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."
NETI@home collects network performance statistics from end-systems.
The related work includes
models of user behavior.
is an open-source, cross-platform network packet/traffic
generator and analyzer.
Swing traffic generator:
K. Viashwanath and A. Vahdat,
Realistic and Responsive Network Traffic Generation,
"Starting from observed traffic at a single point in the network, Swing
automatically extracts distributions for user, application, and network
D-ITG, Distributed Internet Traffic Generator for testbeds:
D-ITG produces traffic "accurately replicating appropriate stochastic
processes for both IDT (Inter Departure Time) and PS (Packet Size)",
and is capable of generating traffic at the network, transport, and
application layers. Includes DCCP support. Updated 2006.
Harpoon traffic generator for testbeds:
J. Sommers and P. Barford,
Self-Configuring Network Traffic Generation, IMC 2004.
Harpoon is an application-independent tool for
generating representative packet traffic at the IP flow level.
Harpoon can also self-configure from
Netflow logs. December 2005.
The Surge traffic generator for testbeds:
Paul Barford and Mark Crovella.
Generating Representative Web Workloads for Network and Server
Performance Evaluation. In Proceedings of the ACM SIGMETRICS, pages
151-160, Madison WI, November 1998. ACM.
Kun-chan Lan and John Heidemann,
Rapid Model Parameterization from Traffic Measurements,
ISI-RF-561, August 2002.
"We describe approaches and tools that support rapid parameterization of
traffic models from live network measurements."
B. Mah, P. Sholander, L. Martinez, and L. Tolendino.
IPB; an Internet Protocol Benchmark using Simulated Traffic.
Proceedings of MASCOTS '98, Montreal, Canada, August 1998. IEEE.
"We have developed an IP Benchmark (IPB), which uses synthetic,
traffic to measure the peformance across an IP network or internetwork."
trafgen traffic generator:
Rigoberto Chinchilla, John Hoag, David Koonce, Hans Kruse,
Shawn Ostermann, and Yufei Wang,
Characterization of Internet Traffic
and User Classification: Foundations for the Next Generation of Network
Proceedings of the 10th International Conference on
Telecommunication Systems, Modeling and Analysis (ICTSM10), 2002.
"Currently, we model traffic based on applications, such as a web
browser or a file transfer application. In the present upgrade,
we are modeling the way users utilize multiple applications."
Benchmarking of Web-Server Systems:
Michele Colajanni, Mauro Andreolini, and Valeria Cardellini,
Benchmarking of Locally and Geographically Distributed Web-Server
Systems, Half-day tutorial at 12th International World Wide Web
Conference (WWW2003), Budapest, Hungary, May 20th, 2003.
GenSyn is a Java-based, traffic generator
generating TCP connections and UDP streams. From 2000.
httperf "provides a flexible facility for generating various HTTP
workloads and for measuring server performance." From 1998.
Modeling Peer-to-Peer Traffic.
Modeling Traffic from Online Games.
Modeling DDoS Attacks.
M. Yuksel, B. Sikdar, K. S. Vastola, and B. Szymanski.
Workload Generation for NS Simulations of Wide Area Networks and
Proceedings of Communication Networks and Distributed Systems
Modeling and Simulation Conference (CNDS) part of Western
Multi-Conference (WMC), pages 93-98, San Diego, CA, 2000.
"We introduce methodologies for implementing realistic workload
generators for wide area networks which (1) maintain the proper composition
of the aggregate traffic resulting from the mix of various applications
supported by the network and (2) are capable of generating
long range dependent or self-similar traffic."
F. Hernandez Campos, K. Jeffay, F.D. Smith, S. Marron, and A. Nobel,
Methodology For Developing Empirical Models of
TCP-Based Applications, 2001.
"We report on a large-scale empirical study to
create application-level models for TCP traffic generation in
simulations and network test-beds."
F. Hernandez-Campos, K. Jeffay, and F. Donelson Smith,
Tracking the Evolution of Web Traffic: 1995-2003,
"These results demonstrate that usage of the web
by both consumers and content providers has evolved significantly
and make a compelling case for continual monitoring
of web traffic and updating of models of web traffic."
Why Traffic Models Matter:
Y. Joo, V. Ribeiro, A. Feldmann, A. C. Gilbert, and W. Willinger,
TCP/IP Traffic Dynamics and Network Performance: A Lesson in Workload
Modeling, Flow Control, and Trace-driven Simulations. CCR, April 2001.
"The main objective of this paper is to demonstrate in the context
of a simple TCP/IP-based network that depending on the underlying
assumptions about the inherent nature of the dynamics of network
very different conclusions can be derived for a number of well-studied
apparently well-understood problems in the area of performance
evaluation. For example, a traffic workload can either completely
the empirically observed high variability at the
TCP connection level (i.e., assume "infinite sources")
or explicitly account for it with the help of heavy-tailed
distributions for TCP connection sizes or durations."
- Usage Patterns in Wireless Networks:
Packet replay engines include
- Commercial traffic generators:
BreakingPoint's test equipment generates up to 15 million TCP
sessions, with more than four thousand security strikes and seventy application
LANforge FIRE traffic generator includes SIP, H.323, VoIP, and RTP,
along with FTP, HTTP, SMTP, and others.
software IP packet generators.
Traffic Generation System (TGS)
includes malicious traffic, web traffic, email, FTP, IRC.
an archive for wireless data.
Thanks to Senthilkumar Ayyasamy for contributions to this page.
Proposed additions to this page can be sent to
This material is based upon work supported by the National Science
Foundation under Grant No. 0230921.
Any opinions, findings, and conclusions or recommendations expressed
in this material are those of the author(s) and do not necessarily
reflect the views of the National Science Foundation.
Last modified: September 2010.
[This web page is no longer actively maintained.]