A prediction module to optimize scheduling in a grid computing environment
Heterogeneous computing environment such as grid computing allows sharing and aggregation of a wide variety of geographically distributed computational resources (such as supercomputers, clusters, data sources, people and storage systems) and present them as a single, unified resource for solvi...
| Main Authors: | , , , |
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| Format: | Conference or Workshop Item |
| Language: | English |
| Published: |
IEEE
2008
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| Subjects: | |
| Online Access: | http://irep.iium.edu.my/50162/ http://irep.iium.edu.my/50162/ http://irep.iium.edu.my/50162/ http://irep.iium.edu.my/50162/1/atrizedcopy.pdf |
| Summary: | Heterogeneous computing environment such as grid
computing allows sharing and aggregation of a wide
variety of geographically distributed computational
resources (such as supercomputers, clusters, data
sources, people and storage systems) and present them
as a single, unified resource for solving large-scale
and data-intensive computing applications. A common
problem arising in grid computing is to select the most
efficient resource to run a particular program. Also
users are required to reserve in advance the resources
needed to run their program on the grid. At present the
execution time of any program submission depends on
guesswork by the user. This leads to inefficient use of
resources, incurring extra operation costs such as
idling queues or machines. Thus a prediction module
was designed and developed to aid the user. This
module estimates the execution time of a program by
using aspects of static analysis, analytical
benchmarking and compiler based approach. It
consists of 4 main stages; each with its own
functionality. An incoming program is categorized
accordingly, parsed and then broken down into smaller
units known as tokens. The complexity and relationship
amongst these tokens are then analyzed and finally the
execution time is estimated for the entire program that
was submitted. |
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