A Self-Adaptive Layered Sleep-Based Method for Security Dynamic Scheduling in Cloud Storage

Optimizing security dynamic scheduling method in cloud storage is significant for improving resource date throughput and storage space of a cloud storage system. The paper presents a self-adaptive layered sleep vision-based method for security dynamic scheduling in cloud storage. A decision-making tree model is used for feature classification of cloud storage resources scheduling, top-down analytical method is used for multithread space reconstruction of resources in cloud storage, and self-adaptive filtering algorithm is used to remove redundant resources. The method of adaptive layered sleep vision is introduced to complete scheduling of cloud storage resources and to improve efficiency of resource scheduling. It is indicated by the simulation result that security and dynamic scheduling in cloud storage with the improved method improves accuracy and efficiency of security resources scheduling in cloud storage, showing relatively high resource utilization.