An algorithm for jobs scheduling in computational grid based on time-balancing strategy

The computational grid provides a promising platform for the efficient execution of parallel coarse grain tasks computing over very large sample space. Scheduling such applications is challenging for the heterogeneity, autonomy, and dynamic adaptability of grid resources. Assuming resource owners have a preemptive priority, we propose an adaptive algorithm of jobs scheduling based on time balancing strategy, which solves the parallel computing tasks by using the idle resources in computational grid. A mathematical model is developed to predict performance, which also considers systems with heterogeneous machine utilization and heterogeneous service distribution. The model separates the influence of machine utilization, job service rate and parallel task allocation on the completion time. According to the time balancing policy, a task is partitioned into several subtasks and scheduled, and the costs of communication are reduced. The expected value of parallel task completion time is predicted with performance model. To get better parallel computing performance, an optimal subset of heterogeneous resources with the shortest parallel executing time of tasks can be selected with the efficient algorithm. Remapping strategy is applied during scheduling, which is more suitable for the dynamic adaptability and domain autonomy in the grid.