Parallel Computing in Electrical Engineering, 2004. International Conference on (2006)
Sept. 13, 2006 to Sept. 17, 2006
Faezeh Montazeri , University of Tehran, Iran
Mehdi Salmani-Jelodar , University of Tehran, Iran
S. Najmeh Fakhraie , University of Tehran, Iran
S. Mehdi Fakhraie , University of Tehran, Iran
The genetic algorithm has, to date, been applied to a wide range of problems. It is an ideal tool to solve problem in need of multiple, often interdependent requirements. This is because it has the ability to search within a large solution space while at the same time meeting criteria and constraints within the problem?s boundaries. In this paper, we apply this heuristic to the problem of multiprocessor task scheduling - assigning a group of predefined tasks to a set of predefined processors. This task execution should take a minimum amount of time while taking into account certain constraints - e.g., prerequisite constraints between the tasks. Aside from using the genetic algorithm, we incorporate a local search method called a memetic within the genetic algorithm as a global search. Since the tasks are operating in a multiprocessor environment, we also attempt to reduce processor temperature by reducing the total power consumption and load balancing amongst the processors.
F. Montazeri, S. N. Fakhraie, S. M. Fakhraie and M. Salmani-Jelodar, "Evolutionary Multiprocessor Task Scheduling," International Symposium on Parallel Computing in Electrical Engineering(PARELEC), Bialystok, 2006, pp. 68-76.