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.
Faezeh Montazeri, Mehdi Salmani-Jelodar, S. Najmeh Fakhraie, S. Mehdi Fakhraie, "Evolutionary Multiprocessor Task Scheduling", Parallel Computing in Electrical Engineering, 2004. International Conference on, vol. 00, no. , pp. 68-76, 2006, doi:10.1109/PARELEC.2006.37