An Efficient Task Priority Measurement for List-Scheduling in Multiprocessor Environments

Optimized task scheduling is one the most important factors to achieve highperformance in multiprocessor environments such as parallel and distributed systems. A large number of proposed approaches to solve this problem use list-scheduling technique in which a list of tasks is created based on the some priority measurements, and then in each step, the most priority task in the list is selected to schedule on the processor that allows the earliest start time. Therefore, the achieved schedule length is highly coherent with how order the tasks are selected to execute. Whereas selected task priority measurement determines which task order would be extracted, in this paper, we survey five traditional task priority measurements named height-level (HL), top-level (TL), bottom-level (BL), static-level (SL) and as-late-as-possible (ALAP) which have been extensively used in the different list-scheduling approaches. In addition, a new efficient task priority measurement based on the number-ofoffspring (NOO) of tasks in task-graph is introduced and evaluated beside the others. The evaluation is made by doing various experiments on random task-graphs with different shape parameters and task-graphs of real-world programs using measures such as normalized schedule length (NSL), pair-wise and global comparison and best solution. Based on the results, it can generally be seen that the proposed NOO is the best, HL, BL, SL and ALAP are temperate (their ranks are slightly changed based on the selected comparison measure), and TL has the worst performance.