Enhanced ant colony optimization for grid resource scheduling
Scheduling jobs to resources in grid computing is complicated due to the distributed and heterogeneous nature of the resources. Stagnation in grid computing system may occur when all jobs require or are assigned to the same resources which will lead to the resources having high workload. Stagnat...
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| Main Authors: | , |
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| Format: | Conference or Workshop Item |
| Language: | English |
| Published: |
2010
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| Subjects: | |
| Online Access: | http://repo.uum.edu.my/3997/1/Husna_%26_Ku_Ruhana.pdf http://repo.uum.edu.my/3997/ |
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| Institution: | Universiti Utara Malaysia |
| Language: | English |
| Summary: | Scheduling jobs to resources in grid computing is complicated due to the distributed and heterogeneous nature
of the resources. Stagnation in grid computing system may
occur when all jobs require or are assigned to the same
resources which will lead to the resources having high
workload. Stagnation also may occur if the computational
time of the processed job is high. An effective job scheduling algorithm is needed to avoid or reduce the stagnation problem. An Enhanced Ant Colony Optimization (EACO) technique for jobs and resources scheduling in grid computing is proposed in this paper. The proposed algorithm combines the techniques from Ant Colony System and Max - Min Ant System and focused on local pheromone trail update and trail limit. The agent concept is also integrated in this proposed technique for the purpose of updating the grid resource table. This facilitates in scheduling jobs to available resources efficiently which will enable jobs to be processed in minimum time and also balance all the resource in grid system. |
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