How to schedule a DAG onto a given number of machines
dag_evaluator and task_scheduler are both python scripts.
(1) dag_evaluator takes task description file as the input and provides the optimal machine number for executing a makeflow.
Usage examples of dag_evaluator:
(1.1) The task dependency relationship and task length is known in advance.
./dag_evaluator -d full bwamf_list2
./dag_evaluator -d full case1_list
./dag_evaluator -d full case2_list
(1.2) The task dependency relationship is known in advance, and the tasks in the same level take similar time, but task length is not known in advance.
./dag_evaluator -d half case1_list_no_length
./dag_evaluator -d half case2_list_no_length
./dag_evaluator -d half case3_list
./dag_evaluator -d half case4_1
(1.3) The task dependency relationship is known in advance, and the tasks in the same level take different times, but task length is not known in advance.
Optimized Machine Number = max{dag_width, average_dag_width}.
./dag_evaluator -d zero case1_list_no_length
./dag_evaluator -d zero case2_list_no_length
./dag_evaluator -d zero case3_list
./dag_evaluator -d zero case4_1
(1.4) The task dependency relationship is known in advance, and the tasks in the same level take different times, but task length is not known in advance.
Optimized Machine Number = maximal parallel running task number (brute force algorithm, expensive)
./dag_evaluator -d zero_brute_force case2_list_no_length
./dag_evaluator -d zero_brute_force case3_list
./dag_evaluator -d zero_brute_force case4_1
(2) task_scheduler takes task description file and a list of achine number as the input, and calculates the execution time, cost, wastage of running the makeflow on each machine number.
Usage examples of task_scheduler:
./task_scheduler -m '45, 50, 55' bwamf_list2
./task_scheduler -m '30, 40, 50, 60' case1_list
./task_scheduler -m '3, 4, 5' case2_list