Abstract: With the increasing of the clusters and the user’s QoS in the cloud environment,it becomes much harder to meet the requirements of jobs and users using the traditional strategy.To adjust scheduler dynamically according to the status of the jobs and the resources,this paper proposed a dynamic scheduling method based on the job classification method in the Hadoop platform.The proposed method employs the Nave Bayesian method to classify the jobs in which the human inferences are added to preset the jobs’ weight according to the types.Then, the scheduling priority of the jobs is set dynamically using the utility function based on the user’s expected completing time and the estimated completed time of jobs.The experimental results show that the proposed method can not only reduce the classification time,but also improve the scheduling dynamics and user’s QoS greatly.

Key words: Human-computer interaction,Hadoop,Dynamic scheduling,Bayesian network,QoS