Scheduling workflows in opportunistic environments

Workflow applications exhibit both high computation times and data transfer rates. For this reason, the completion time of the workflow is high. To reduce completion time, the tasks of a workflow ought to run on different machines interconnected by a network. Correct assignment of tasks to machines within the runtime environment is an important aspect in the completion time or make span. The manager making the assignment is the scheduler. The main problem of a static scheduler is that it ignores the changes that occur in the execution environment during DAG execution. To solve this problem, we developed a new dynamic scheduler. This dynamic scheduler monitors the behavior of the tasks executed as well as the execution environment, and it reacts to the changes detected by adapting the scheduling of the rest of pending tasks. The objective is to reduce the overhead incurred by excessive self-adaptations, without affecting the make span. To reduce overhead, the algorithm self-adapts only when an improvement in make span is expected. The proposed policies have been simulated and then executed in a real environment. These executions have achieved a reduction of the overhead of greater than 20%.