Enhanced ant colony optimization for grid resource scheduling

• Main Authors: Abdul Nasir, Husna Jamal, Ku-Mahamud, Ku Ruhana
• Format: Conference or Workshop Item
• Language: English
• Published: 2010
• Subjects: QA75 Electronic computers. Computer science
• Online Access: http://repo.uum.edu.my/3997/1/Husna_%26_Ku_Ruhana.pdf; http://repo.uum.edu.my/3997/
• Institution: Universiti Utara Malaysia

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.