University of Houston
Department of Computer Science
In Partial Fulfillment of the Requirements for the Degree of Doctor of Philosophy
Yonghong Yan
Will defend his dissertation
Scheduling Scientific Workflow Applications in Computational Grids
Abstract
A computational grid, built using grid computing
technology, is a network of computing resources that work together as a single,
uniform operating environment. It can be viewed as a virtual supercomputer
designed for large-scale applications. The execution of these applications
involves the concurrent and sequential execution of multiple modules in a
predefined order and the automatic and timely data transfer between modules.
These applications are often referred to as scientific workflow applications.
A very important issue in executing a scientific workflow application in computational grids is how to map and schedule workflow modules onto multiple distributed resources and handle module dependencies in a timely manner to deliver users' expected performance. The goal of this research is to develop a workflow system to address this in computational grid environments. In our work, we have developed a grid workflow description language that addresses the limitation of lacking resource request specification in current related efforts. An integrated workflow scheduling architecture has been defined that provides the capabilities of workflow execution planning, resource allocation and execution coordination. Our workflow scheduler applies the advanced scheduling techniques, such as planning, resource reservation and performance predictions in the resource allocation process. The simulation results show that our workflow scheduler reduces the workflow execution time for about 20% averagely under high resource load, compared to the scheduling policies used in most of current workflow systems.
A very important issue in executing a scientific workflow application in computational grids is how to map and schedule workflow modules onto multiple distributed resources and handle module dependencies in a timely manner to deliver users' expected performance. The goal of this research is to develop a workflow system to address this in computational grid environments. In our work, we have developed a grid workflow description language that addresses the limitation of lacking resource request specification in current related efforts. An integrated workflow scheduling architecture has been defined that provides the capabilities of workflow execution planning, resource allocation and execution coordination. Our workflow scheduler applies the advanced scheduling techniques, such as planning, resource reservation and performance predictions in the resource allocation process. The simulation results show that our workflow scheduler reduces the workflow execution time for about 20% averagely under high resource load, compared to the scheduling policies used in most of current workflow systems.
Date: Monday, April 23, 2007
Time: 2:30 PM
Place: 550-PGH
Faculty, students, and the general public are invited.
Advisor: Prof. Barbara M. Chapman