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Abstract
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Article Information:
Data and Job Aware Community Scheduler Framework for Grid Scheduling Problems
G. Kalpana and D.I. George Amalarethinam
Corresponding Author: G. Kalpana
Submitted: September 22, 2014
Accepted: October 24, 2014
Published: December 20, 2014 |
Abstract:
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Grid technologies have brought the promise of flawless combination of distributed heterogeneous resources. Scheduling in Grid computing is the hot topic of research and challenging to manage task, job in efficient manner. For instance, tasks are assumed to include all data needed for its computation or tasks are just the processes and data is assumed to be available in Grid nodes. All the existing works doesn’t focus on the data aware scheduling framework and it is almost impossible to make an optimal or approximate optimal scheduling for the end-to-end workflow with considering the intermediate data movement in grid computing environment. In order to solve this problem in this study a novel Community Scheduler Framework (CSF) approach is proposed for solving the job and data aware scheduling problem together and it can be integrated to grid host environment. The system is able to find data-affinity hosts for user requested jobs and to adjust the data replicas dynamically according to the job load. The proposed work reviews the policies for scheduling of grid jobs in the context of data and task aware-intensive applications. Proposed data and task aware CSF Meta scheduler framework makes it possible for job requests to set data needs not only as absolute requirements but also as functions for resource ranking. As the experimental results show that, this makes it more flexible than currently used resource brokers to implement different data-aware scheduling algorithms. The experimentation of the proposed Meta scheduler work is implemented with the help of grid simulation toolkit.
Key words: Community Scheduler Framework (CSF), data aware scheduling, grid computing, grid simulation toolkit, Job aware scheduling, scheduling,
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Cite this Reference:
G. Kalpana and D.I. George Amalarethinam, . Data and Job Aware Community Scheduler Framework for Grid Scheduling Problems. Research Journal of Applied Sciences, Engineering and Technology, (23): 2334-2342.
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ISSN (Online): 2040-7467
ISSN (Print): 2040-7459 |
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