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Abstract
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Article Information:
Intelligent Water Drop Algorithm Based Particle Swarm Optimization (IWDPSO) Towards Multi Objective Job Scheduling for Grid Computing
D. Thilagavathi and Antony Selvadoss Thanamani
Corresponding Author: D. Thilagavathi
Submitted: October 29, 2014
Accepted: December 18, 2014
Published: April 15, 2015 |
Abstract:
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The development of a huge amount of client’s job for equivalent performance on open-resource grid system is the main reason of system failures or delayed process due to grimy hardware, software vulnerability, as well as shared confined policy. In this study we represent highly reliability conditions in grid work scheduling and present a new procedure for scheduling by hybridization of intelligent water drop algorithm and particle swarm optimization technique and compare it with earliest deadline in the basis of first come first served. The IWDPSO algorithm is tested with two datasets namely Numerical Aerodynamic Simulation (NAS) and Parameter Sweep Application (PSA) and the results are tested with performance metrics makespan, slowdown and failure rate and grid utilization. The proposed algorithms results in effective usage of grid computing resources with reduced makespan, slowdown and failure rate. The proposed algorithm is compared with Risky-MinMin (RMM), Preemptive-MinMin (PMM) and Delay Tolerant Space-Time Genetic Algorithm (DTSTGA).
Key words: Grid, IWD, IWDPSO, job scheduling, NAS, PSA, PSO
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Cite this Reference:
D. Thilagavathi and Antony Selvadoss Thanamani, . Intelligent Water Drop Algorithm Based Particle Swarm Optimization (IWDPSO) Towards Multi Objective Job Scheduling for Grid Computing . Research Journal of Applied Sciences, Engineering and Technology, (11): 982-989.
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ISSN (Online): 2040-7467
ISSN (Print): 2040-7459 |
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