Institute for Systems Research Technical Reports

Permanent URI for this collectionhttp://hdl.handle.net/1903/4376

This archive contains a collection of reports generated by the faculty and students of the Institute for Systems Research (ISR), a permanent, interdisciplinary research unit in the A. James Clark School of Engineering at the University of Maryland. ISR-based projects are conducted through partnerships with industry and government, bringing together faculty and students from multiple academic departments and colleges across the university.

Browse

Filters

20001

Settings

Author: search.filters.author.Barrett, Bradley A.Subject: search.filters.subject.hybrid networks

Search Results

Now showing 1 - 1 of 1
  • Thumbnail Image
    Item
    Traffic Models for Hybrid Satellite-Terrestrial Networks
    (2000) Barrett, Bradley A.; Baras, John S.; ISR; CSHCN
    While Hybrid Satellite-Terrestrial Networks (HSTNs) have become a popular method of providing Internet connectivity, network dimensioning and performance prediction problems in these networkss in their terrestrial counterpartsave remain largely unsolved. A key hindrance to the resolution of these issues has been accurate, tractable traffic models. While a number of rather complex models have been proposed for terrestrial network traffic, these have not been evaluated against HSTN traffic. And further, recent studies have questioned whether these more complex models, while statistically better fits, really provide significantly better performance prediction.

    We examine the question of how to model HSTN traffic for network dimensioning and performance prediction, and in particular, how far ahead into the future a traffic model can be expected to accurately function. We investigate these issues by directly comparing four of the most likely candidate statistical distributionshe exponential, log-normal, Weibull and Pareto. These distributions are fit to two key traffic parameters from real HSTN traffic traces (connection interarrival times and downloaded bytes), and their relative fits are compared using statistical techniques. We further compare traffic models built using these distributions in a simulated environment; comparing performance predictions (over a number of metrics) obtained from these models to the actual results from our real-world traffic traces.