An optimization scheduling model for multi-user software under cloud computing environment

The multi-user software scheduling optimization under cloud computing environments is studied. Under the cloud computing environment, multi-user scheduling software possessed characteristics like multi-objective, multi-constraint and dynamic, the traditional Particle Swarm Algorithm can only be utilized to schedule for the single-user software, therefore, multi-user software scheduling algorithm based on genetic - ant colony algorithm under cloud computing environment is proposed. To start with, a multi-user software scheduling mathematical model is established, then the genetic algorithm is introduced to quickly find a feasible solution for multi-user software scheduling, and finally the feasible solution obtained through the genetic algorithm is converted into Ant Colony Optimization (ACO) initial pheromone, and the optimal solution for multiuser software scheduling is acquired through local optimization and a positive feedback mechanism of ant colony algorithm. Simulation results show that the improved algorithm has not only the global optimization capability of genetic algorithm, also possess local optimization and the positive feedback capability of ant colony algorithm, compared to a single optimization algorithm, it can quickly find scheduling solutions to meet real-time requirements, augment scheduling speed, which favors scheduling for multi-user software reasonably and effectively.