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Faculty of Computing and Information Technology
Document Details
Document Type
:
Article In Conference
Document Title
:
Predicting the execution time of grid workflow applications through local learning
التنبؤ بوقت التنفيذ الخاص بتطبيقات تدفق الأعمال للأنظمة الشبكية باستخدام عملية التعلم المحلية
Subject
:
Performance Modeling of Scientific Workflow Applications
Document Language
:
English
Abstract
:
Workflow execution time prediction is widely seen as a key service to understand the performance behavior and support the optimization of Grid workflow applications. In this paper, we present a novel approach for estimating the execution time of workflows based on Local Learning. The workflows are characterized in terms of different attributes describing structural and runtime information about workflow activities, control and data flow dependencies, number of Grid sites, problem size, etc. Our local learning framework is complemented by a dynamic weighing scheme that assigns weights to workflow attributes reflecting their impact on the workflow execution time. Predictions are given through intervals bounded by the minimum and maximum predicted values, which are associated with a confidence value indicating the degree of confidence about the prediction accuracy. Evaluation results for three real world workflows on a real Grid are presented to demonstrate the prediction accuracy and overheads of the proposed method.
Conference Name
:
SC '09: International Conference on High Performance Computing
Duration
:
From : 27/11/1430 AH - To : 3/12/1430 AH
From : 14/11/2009 AD - To : 20/11/2009 AD
Publishing Year
:
1430 AH
2009 AD
Number Of Pages
:
11
Article Type
:
Article
Conference Place
:
Portland, USA
Added Date
:
Monday, July 16, 2012
Researchers
Researcher Name (Arabic)
Researcher Name (English)
Researcher Type
Dr Grade
Email
فاروق نديم
Nadeem, Farrukh
Investigator
Doctorate
abdullatif@kau.edu.sa
Thomas Fahringer
Fahringer, Thomas
Researcher
Files
File Name
Type
Description
33974.pdf
pdf
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