A Genetic Algorithm for mapping tasks in heterogeneous computing systems

Heterogeneous computing systems require an efficient way of distributing tasks across processing nodes. The tasks have to be mapped to the processors which execute them in the shortest time possible, while keeping the processors at a similar load. Tests have shown that, in most cases, the genetic algorithm produces the best solution among all the mapping heuristics. This paper presents a Genetic Algorithm with a 3-Step Mutation which significantly increases the solution's convergence rate by using a combination of methods to mutate a chromosome. Beside the standard random approach, we implemented a targeted mutation operator which lightens the load of the most occupied processors. We also focused on different fitness functions in order to improve both the makespan and the load balance. The mutation combinations and the fitness functions are then tested to see which ones perform better and in what cases.