Paper Info
Title
Scientific workflow scheduling in computational grids Planning, reservation, and data/network-awareness
Abstract
A very important issue in executing a scientific workflow in computational grids is how to map and schedule workflow tasks onto multiple distributed resources and handle task dependencies in a timely manner to deliver users’ expected performance. In this paper, we present our work to develop and evaluate an advanced workflow scheduler in computational grid environments, the GRACCE scheduler. The GRACCE scheduler applies advanced scheduling techniques, such as resource negotiation and reservation, data/network-aware scheduling and performance prediction in the resource allocation and execution planning process. To evaluate the scheduler, we have set up an experimental environment that models a computational grid in those aspects relevant to workflow scheduling. Our results show the average performance improvement, using the GRACCE scheduler, is about 20% under high resource loads.
Year
DOI
Venue
2007
10.1109/GRID.2007.4354111
GRID
Keywords
Field
DocType
expected performance,scientific workflow scheduling,average performance improvement,advanced scheduling technique,computational grids planning,computational grid environment,scientific workflow,gracce scheduler,schedule workflow task,computational grid,advanced workflow scheduler,high resource load,resource allocation,planning,scheduling,grid computing
Fixed-priority pre-emptive scheduling,Workflow technology,Grid computing,Computer science,Scheduling (computing),Real-time computing,Resource allocation,Workflow engine,Workflow,Workflow management system,Distributed computing
Conference
ISSN
ISBN
Citations 
2152-1085
978-1-4244-1559-5
6
PageRank 
References 
Authors
0.45
29
2
Name
Order
Citations
PageRank
Yonghong Yan1656114.13
Barbara Chapman216314.63