Optimal Multi-Resource Scheduling Strategy Simulation Based on Improved Genetic Algorithm

In order to prevent large-scale cloud space scheduling conflict, achieve reasonable dispatch of cloud computing resources. By conducting a detailed analysis of the cloud resources scheduling process, improved genetic algorithm is proposed based on cloud computing resource scheduling model. In this model, firstly, the resource scheduling sequences of cloud computing are encoding into chromosomes. Then in the scheduling process, load balancing degree of the cloud computing model is regarded as the optimization objective, aiming at the non-optimal problem occurred in scheduling process. By genetic algorithm selection, crossover and mutation operation, continue to search to find the optimal cloud computing resources scheduling scheme. Finally, simulation is operated on CloudSim platform. Simulation results show that, compared to traditional particle swarm optimization algorithm, which basically meet the requirements of automatic scheduling algorithm in the cloud computing environment, such as stability, reliability and high precision, not only improves resource utilization of cloud computing, but also shortens task completion time, while for rational management study of cloud computing resources provides theoretical reference, and promotes the continuous development of the research field.