Disclosed is a genetic algorithm-based cloud computing resource scheduling method. The present invention utilizes a genetic algorithm to acquire a resource scheduling solution in cloud computing. Each of genes of a chromosome in the genetic algorithm is a subtask in a cloud task. A bit number of the chromosome indicates a number allocated to a virtual apparatus by each of the subtask representative. The present invention sets an expected Qos according to the cloud task, retains a chromosome satisfying the expected Qos in a process of population iteration and update, discarding a chromosome not satisfying the expected Qos, directly copies a chromosome having a high current population fitness value to a next generation of population according to a selection ratio, then performs crossover and mutation operations with respect to the rest of the chromosomes of the current population. The present invention ensures the quality of a chromosome in the population, enables resources in cloud computing to be utilized on-demand and flexibly expanded, arranges cloud computing resource scheduling in real time, selects an optimal policy of resource construction and adjustment, guarantees cloud computing network service quality, and improves an effectiveness ratio of the cloud computing.
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