Self similar traffic generation by method of Random Midpoint Displacement Algorithm (RMDA), 2002

Item — Call Number: MU Thesis Huf

Identifier: b2089935

Staff Only

Scope and Contents

From the Collection:

The collection consists of theses written by students enrolled in the Monmouth University graduate Electrical Engineering program. The holdings are bound print documents that were submitted in partial fulfillment of requirements for the Master of Science degree.

Dates

Creation: 2002

Creator

Huff, Walter (Author, Person)
Drucker, Harris (1943-2024) (Thesis advisor, Person)

Conditions Governing Access

The collection is open for research use. Access is by appointment only.

Access to the collection is confined to the Monmouth University Library and is subject to patron policies approved by the Monmouth University Library.

Collection holdings may not be borrowed through interlibrary loan.

Research appointments are scheduled by the Monmouth University Library Archives Collections Manager (723-923-4526). A minimum of three days advance notice is required to arrange a research appointment for access to the collection.

Patrons must complete a Researcher Registration Form and provide appropriate identification to gain access to the collection holdings. Copies of these documents will be kept on file at the Monmouth University Library.

Extent

1 Items (print book) : 24 pages ; 8.5 x 11.0 inches (28 cm).

Language of Materials

English

Additional Description

Abstract

This paper explores an algorithmic means for simulating self-similar data traffic, which can be used to model broadband networks. The aim of this work is to create a traffic generator tool that generates stochastic self-similar traffic using a fast simulation technique known as Random Midpoint Displacement Algorithm (RMDA). Finally, the output of the RMDA traffic generator is evaluated for accuracy by using the Varience of Residuals method to compare the Hurst estimates to the actual values utilized in the RMDA traffic generation. Subsequently, MatLaB programs were implemented for test of the recommended generation and evaluation methods.

Partial Contents

List of figures -- Introduction -- Theory behind self-similarity -- The "RMDA" method -- Qualitative and Quantitative results of RMDA -- Techniques for consderation -- Conclusion -- Appendix -- References.