Workflow tasks scheduling optimization based on genetic algorithm in clouds

Tasks scheduling problem is the key challenge in cloud computing system. For reducing the execution cost of workflow tasks scheduling under the deadline and the budget constraint, a workflow tasks scheduling algorithm based on genetic algorithm in cloud computing is proposed. In our algorithm, each task is assigned priority by an top-down leveling method. By this top-down leveling method, all workflow tasks are divided into the different levels, which can promote the parallel execution of workflow tasks. When code the solution of tasks scheduling, we design a two dimension coding method. And, we design a new genetic crossover and mutation operation to produce new different off springs for increasing the population diversity. Through the fitness function synchronously considering the scheduling time and the scheduling cost, we can evaluate the individual fitness of population. Through the simulation experiments, we evaluate the performance of our algorithm based on realistic workflows model. The results show that our algorithm has a better performance in reducing the workflow scheduling cost.