Wednesday 9 December 2009

Wseas Transactions

New Subscription to Wseas Transactions

The following information was submitted:

Transactions: INTERNATIONAL JOURNAL of COMPUTERS AND COMMUNICATIONS
Transactions ID Number: 19-210
Full Name: Candelaria Sansores
Position: Doctor (Researcher)
Age: ON
Sex: Female
Address: SM.78 Mz.1 LT.1 esquina Fracc. Tabachines, Cancún, Q. Roo, México, CP. 77528
Country: MEXICO
Tel:
Tel prefix:
Fax:
E-mail address: csansores@ucaribe.edu.mx
Other E-mails: csansores@fdi.ucm.es
Title of the Paper: what is the required number of users for the generation of aggregated H-ss traffic?
Authors as they appear in the Paper: C. Sansores-Perez, L. Rizo-Dominguez and J. Ramirez-Pacheco
Email addresses of all the authors: csansores@ucaribe.edu.mx, lrizo@ucaribe.edu.mx, jramirez@ucaribe.edu.mx
Number of paper pages: 8
Abstract: It is well known that network traffic can be well modeled by the use of self-similar processes with parameter H. The use of this kind of traffic is important for the design and performance evaluation of high performance computer networks. Simulation plays a very important role in the context of performance analysis. In the context of simulation, however, the impact of the number of sources has not been sufficiently emphasized for the generation of synthetic self-similar traffic. In this paper we describe a simulation scenario suitable for the testing of performance issues under self-similar traffic. Our analysis was centered on the effect of traffic aggregation over the self-similarity degree, determining the necessary number of sources to approach the verified relation H = (3 − min )/2. Besides, we highlighted the performance of several Hurst parameters estimators for this type of simulation scenarios, identifying the most suited ones.
Keywords: Self-similar traffic, Heavy-tail distributions, Performance evaluation, Simulation, Hurst Parameter
EXTENSION of the file: .pdf
Special (Invited) Session: A Simulation Scenario for Performance Studies Under Self-Similar Traffic
Organizer of the Session: 639-227
How Did you learn about congress:
IP ADDRESS: 189.149.16.81