Grid computing utilizes the distributed heterogeneous resources in order to support complicated computing problems. Grid can be classified into two types: computing grid and data grid. Job scheduling in computing grid is a very important problem. To utilize grids efficiently, we need a good job scheduling algorithm to assign jobs to resources in grids.

In the natural environment, the ants have a tremendous ability to team up to find an optimal path to food resources. An ant algorithm simulates the behavior of ants. In this paper, we propose a Balanced Ant Colony Optimization (BACO) algorithm for job scheduling in the Grid environment. The main contributions of our work are to balance the entire system load while trying to minimize the makespan of a given set of jobs. Compared with the other job scheduling algorithms, BACO can outperform them according to the experimental results.

In this paper, we propose a BACO algorithm to choose suitable resources to execute jobs according to resources status and the size of given job in the Grid environment. The local and global pheromone update functions do balance the system load. Local pheromone update function updates the status of the selected resource after jobs assignment. Global pheromone update function updates the status of each resource for all jobs after the completion of a job. It offers the Job Scheduler the newest information of all resource for the next jobs assignment. The experimental result shows that BACO is capable of balance the entire system load.

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