A hybrid multiprocessor task scheduling method based on immune genetic algorithm
Proceedings of the 5th International ICST Conference on Heterogeneous Networking for Quality, Reliability, Security and Robustness
A bipartite genetic algorithm for multi-processor task scheduling
International Journal of Parallel Programming
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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.