Schedulability-guided exploration of multi-core systems

Efficient mapping of tasks onto heterogeneous multi-core systems is very challenging especially under hard timing constraints. Assigning tasks to processors is an NP-hard problem and solving it requires the use of meta-heuristics. Relevantly, genetic algorithms have already proven to be one of the most powerful and widely used stochastic tools to solve this problem. Conventional genetic algorithms were initially defined as a general evolutionary algorithm based on blind operators. It is commonly admitted that the use of these operators is quite poor for an efficient exploration. Like-wise, since exhaustive exploration of the solution space is unrealistic, a potent option is often to guide the exploration process by hints, derived by problem structure. This guided exploration prioritizes fitter solutions to be part of next generations and avoids exploring unpromising configurations by transmitting a set of predefined criteria from parents to children. Consequently, genetic operators, such as crossover, must incorporate specific domain knowledge to intelligently guide the exploration of the solution space. In this paper, we illustrate and evaluate the impact of crossover operators and we propose a hybrid genetic algorithm based on a novel schedulability-guided operator that easily outperforms the classical operators by offering at least 21% improvement in terms of ratio of certainly schedulable tasks.