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How to make better resource scheduling has always been a hot topic in the research on cloud computing. In this paper, the Cuckoo Search algorithm is introduced into cloud computing resources algorithm. In view of the phenomena such as fast convergence and frequent local oscillation in the application of Cuckoo Search algorithm, first the Gauss mutation operator is introduced in this paper to handle the selection of optimal nest position in every stage, and then the self-adaptive dynamic factor is used to adjust the selection of nest position in different stages. In this way, the improved algorithm is more precise in convergence. Through the balance of the fitness function and the three kinds of operation in Genetic Algorithm, the algorithm presented in this paper can effectively improve the efficiency of resource allocation in the cloud computing environment, and thus reduce the network consumption. In the simulation experiment on the Cloudsim platform, through the comparison in three aspects, the algorithm of this paper has been improved greatly in performance, efficiency of resource scheduling and task scheduling, which effectively improves the resource scheduling ability of the cloud computing system.