Generational scheduling for dynamic task management in heterogeneous computing systems

Heterogeneous computing (HC) is the coordinated use of different types of machines, networks, and interfaces in order to maximize performance and/or cost effectiveness. In recent years, research related to HC has addressed one of its most fundamental challenges: how to develop a schedule of tasks on a set of heterogeneous hosts that minimizes the time required to execute the given tasks. The development of such a schedule is made difficult by diverse processing abilities among the hosts, data and precedence dependencies among the tasks, and other factors. This paper outlines a straightforward approach to solving this problem, termed generational scheduling (GS). GS provides fast, efficient matching of tasks to hosts and requires little overhead to implement. This study introduces the GS approach and illustrates its effectiveness in terms of the time to determine schedules and the quality of schedules produced. A communication-inclusive extension of GS is presented to illustrate how GS can be used when the overhead of transferring data produced be some tasks and consumed by others is significant. Finally, to illustrate the effectiveness of GS in a real-world environment, a series of experiments are presented using GS in the SmartNet scheduling framework, developed at US Navy's facility at the Naval Command, Control, and Ocean Surveillance Center in San Diego, California.