Virtualization resource scheduling and optimization method based on swarm intelligent systems

This paper deeply discusses the virtual resource scheduling and optimization method based on swarm intelligence algorithm, and compares the experimental performance with other methods. • Swarm intelligence algorithms show significant advantages in the field of virtualization resource scheduling and optimization. Simulating the behavior of biological swarms, the swarm intelligence algorithm shows efficient and adaptive scheduling ability in the virtual complex environment. When the resources are parallel, the optimal solution can be obtained by the cooperation of agents, and the robust operation of the system can be guaranteed. • It surpasses the tradition in resource utilization, response speed and throughput, especially in dealing with large-scale concurrency, with significant time performance efficiency. And is adaptive to a changeable cloud environment. • The increase of the number of virtual machines and physical machines will also have an impact on the number of virtual machine migrations, but the virtual resource scheduling method based on swarm intelligence algorithm can still maintain a low number of virtual machine migrations under different parameter settings. Efficient scheduling of virtualized resources can not only meet the service needs of users, but also achieve the optimal allocation of resources and the stable operation of the system. However, due to the dynamic and diversity of virtualized resources, the traditional scheduling methods have been difficult to meet the actual needs. Therefore, a virtual resource scheduling and optimization method based on Swarm Intelligence System (SIS) is proposed in this paper. The core idea of this method is to transform the Virtualized Resource Scheduling (VRS) problem into a multi-objective optimization problem, and use the particle swarm optimization algorithm of SIS to search for the optimal solution. By updating the speed and position of the particles, the scheduling scheme is optimized iteratively to maximize the utilization of resources and optimize the performance of the system. The experimental results show that the SIS-based virtual resource scheduling method can significantly improve the resource utilization and system performance while meeting the needs of users. Compared with other scheduling methods, this method has better adaptability and robustness, and provides a new solution for virtualization resource scheduling in cloud computing environment.