Dynamic scheduling algorithm maximize task scheduling performance and minimize non reasonable task allocation in clouds, Dynamic scheduling proposes a method based on a two-stage strategy. At the first stage, a job classifier motivated by a Bayes classifier’s design principle is utilized to classify tasks based on historical scheduling data. A certain number of virtual machines (vms) of different types are accordingly created. This can save time of creating vms during task scheduling. At the second stage, tasks are matched with concrete vms dynamically, By using Adaptive Scoring Job Scheduling Algorithm (ASJS) along with it to improve the effectiveness of the process and also reduce the execution cost. Experimental results show that they can effectively improve the cloud’s scheduling performance and achieve the load balancing of cloud resources in comparison with existing methods. Cloud can achieve the same level of computing power as a supercomputer does, but at much reduced cost. Scheduling independent tasks on it is more complicated. In order to utilize the power of cloud computing completely, need an efficient task scheduling algorithm to assign tasks to resources. Dynamic scheduling algorithm focuses on the efficient task scheduling considering the completion time of tasks in a cloud environment.