Task allocation and scheduling are essential for achieving the high performance expected of parallel computing systems. However, there are serious issues pertaining to the efficient utilization of computational resources in such systems that need to be resolved, such as, achieving a balance between system throughput and execution time. Moreover, many scheduling techniques involve massive task graphs with complex precedence relations, processing costs, and inter-task communication costs. In general, there are two main issues that should be highlighted: problem representation and finding an efficient solution in a timely fashion. In the work proposed here, the authors have attempted to overcome the first problem by using a structured model which offers a systematic method for the representation of the scheduling problem. The model used can encode almost all of the parameters involved in a scheduling problem in a very systematic manner. To address the second problem, a Tabu Search algorithm is used to allocate tasks to processors in a reasonable amount of time. The use of Tabu Search has the advantage of obtaining solutions to more general instances of the scheduling problem in reasonable time spans. The efficiency of the proposed framework is demonstrated by using several case studies. A number of evaluation criteria will be used to optimize the schedules. Communication- and computation-intensive task graphs are analyzed, as are a number of different task graph shapes and sizes.
Generating synthetic task graphs for simulating stream computing systems