DMCA
Using Dataflow Models to Evaluate Enterprise Distributed Real-time and Embedded System Quality-of-Service
Cached
Download Links
BibTeX
@MISC{Hill_usingdataflow,
author = {James H. Hill and Pooja Varshneya and Hamilton A. Turner and James R. Edmondson and Douglas C. Schmidt},
title = {Using Dataflow Models to Evaluate Enterprise Distributed Real-time and Embedded System Quality-of-Service},
year = {}
}
Share
 |
 |
 |
 |
||
OpenURL
Abstract
The effort required to evaluate enterprise distribute real-time and embedded (DRE) system qualityof-service (QoS) attributes (such as response-time, latency, and scalability) depends heavily on system complexity and size. As these systems increase in complexity and size, therefore, DRE system developers and testers need improved methods and tools that facilitate QoS evaluation. This article describes a method and tool called Understanding Non-functional Intentions via Testing and Experimentation (UNITE) that evaluates enterprise DRE system QoS attributes using dataflow models to capture how data move through an enterprise DRE system. Empirical results show that although UNITE’s evaluation times depend on the size of the dataflow model, they depend even more on the size of the dataset processed by the dataflow model. Copyright c ○ 2009 John Wiley & Sons, Ltd. Received X August 2009 KEY WORDS: enterprise DRE systems; dataflow models; quality-of-service evaluation; early system integration testing; system execution traces; relational database theory 1.
Keyphrases
dataflow model evaluate enterprise distributed real-time embedded system quality-of-service enterprise dre system dre system developer relational database theory qos evaluation understanding non-functional intention john wiley son system complexity key word early system integration enterprise distribute real-time unite evaluation time system increase system execution trace empirical result quality-of-service evaluation dre system qos
