Dynamic Adjustment Algorithm of Cloud Computing Virtual Cluster under Fragment Interference

The task scheduling algorithm of cloud computing virtual machine topology is studied. First, a new virtual machine model and task model should be proposed, and the establishment of this model must be based on the virtual topology, and then a virtual machine-task fitness evaluation function FEF should be established. When scheduling resources, we should coordinate the relationship between the topological distance between virtual machines and the processing performance of virtual machines. When resources are assigned to the corresponding tasks, the fitness value between the target virtual machine and the task is accurately calculated by using FEF function, and the virtual machine with the highest evaluation value is selected by the calculated result. The experimental results show that this method can effectively reduce the total time to complete the task set, thus ensuring the scheduling performance. Quantify the resource fragments formed by different resource scheduling strategies in the cloud computing environment, and re-divide them quantitatively, so that they can regroup into a whole block of resources to maximize the reception of subsequent tasks. Using membrane computing and bat algorithm, according to the optimization requirements of resource allocation in cloud computing under the interference of debris resources, resources are reasonably allocated on the basis of ensuring the time requirement, and the concept of membrane computing is introduced to obtain the final optimal solution. Simulation results show that the proposed method can effectively improve the resource scheduling ability.