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Simulation of large scale networks III: ROSS.Net: optimistic parallel simulation framework for large-scale internet models
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Proceedings of the 35th conference on Winter simulation: driving innovation table of contents
New Orleans, Louisiana
SESSION: Modeling methodology a table of contents
Pages: 703 - 711  
Year of Publication: 2003
ISBN:0-7803-8132-7
Authors
David Bauer  Rensselaer Polytechnic Institute, Troy, NY
Garrett Yaun  Rensselaer Polytechnic Institute, Troy, NY
Christopher D. Carothers  Rensselaer Polytechnic Institute, Troy, NY
Murat Yuksel  Rensselaer Polytechnic Institute, Troy, NY
Shivkumar Kalyanaraman  Rensselaer Polytechnic Institute, Troy, NY
Sponsors
INFORMS/CS : Institute for Operations Research and the Management Sciences/College on Simulation
NIST : National Institute of Standards and Technology
IEEE/SMCS : Institute of Electrical and Electronics Engineers/Systems, Man, and Cybernetics Society
ACM: Association for Computing Machinery
(SCS) : The Society for Modeling and Simulation International
SIGSIM: ACM Special Interest Group on Simulation and Modeling
IIE : Institute of Industrial Engineers
IEEE/CS : Institute of Electrical and Electronics Engineers/Computer Society
ASA : American Statistical Association
Publisher
Winter Simulation Conference 
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Downloads (6 Weeks): 3,   Downloads (12 Months): 24,   Citation Count: 0
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ABSTRACT

ROSS.Net brings together the four major areas of networking research: network modeling, simulation, measurement and protocol design. ROSS.Net is a tool for computing large scale design of experiments through components such as a discrete-event simulation engine, default and extensible model designs, and a state of the art XML interface. ROSS.Net reads in predefined descriptions of network topologies and traffic scenarios which allows for in-depth analysis and insight into emerging feature interactions, cascading failures and protocol stability in a variety of situations. Developers will be able to design and implement their own protocol designs, network topologies and modeling scenarios, as well as implement existing platforms within the ROSS.Net platform. Also using ROSS.Net, designers are able to create experiments with varying levels of granularity, allowing for the highest-degree of scalability.


REFERENCES

Note: OCR errors may be found in this Reference List extracted from the full text article. ACM has opted to expose the complete List rather than only correct and linked references.

 
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Carothers, C. D., Bauer, D., and S. Pearce, 2002. Ross: a high-performance, low memory, modular time warp system. Journal of Parallel and Distributed Computing 62: 1648--1669.
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Floyd, S. 2001. Simulation is crucial. IEEE Spectrum 38(1): 76, sidebar article.
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Helmy, A., D. Estrin, and S. Gupta. 2000. Systematic testing of multicast routing protocols: Analysis of forward and backward search techniques. In International Conference on Computer Communications and Networks (ICCCN).
 
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Jain, R. 1991. The art of computer systems performance analysis: techniques for experimental design, measurement, simulation, and modeling. Wiley - Interscience.
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Montgomery, D. C. 2001. Design and analysis of experiments. John Wiley and Sons.
 
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Taguchi, G. 1986. Introduction to quality engineering. Asian Productivity Organization, UNIPUB, White Plains, NY.
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Collaborative Colleagues:
David Bauer: colleagues
Garrett Yaun: colleagues
Christopher D. Carothers: colleagues
Murat Yuksel: colleagues
Shivkumar Kalyanaraman: colleagues