Research On Energy - Aware Task Scheduling Strategy In Cloud Computing Environment

Cloud computing is a computing model based on commercial Internet, with dynamic scalability characteristics. Cloud computing environment consists of a series of dynamically scalable resources, the cloud computing service provider will provide these resources to cloud computing users with virtualization technology. Users can lease cloud computing resources on demand, it not only can reduce the processing load on the terminal itself, but also can have a cloud computing power. When people deal with the massive task in cloud computing environment, a reasonable scheduling strategy in cloud computing environments becomes an efficient and effective means to improve the task execution and to fully utilize network resources. Therefore, the research of task scheduling strategy in cloud computing environment has important significance.In this paper, after in-depth research of cloud computing and cloud computing task scheduling strategy in cloud computing environments, in connection with the lack of consideration of user preferences in the existing task scheduling strategy in cloud computing environment, so that the virtual machine resource utilization is not sufficient, user satisfaction of the quality of service is a problem, this paper proposed an energy-awared scheduling strategy in cloud computing environment.The main research work is as follows:(1) Models the task scheduling environment in cloud computing environment for system, which consists of users, tasks, and resources.(2) Defines the task preference index which is calculated with the user task information. The specific method is:First calculate the average value of the user preference of the task; then obtained the deviation rate of task information between the user preference and the average value of preference; Finally, the error rate is stored as user task preference index.(3)Defines the virtual machine resource performance score which is calculated by the performance parameters of the resources properties.The specific method is:First calculate the average performance value of the virtual machine resource properties; then obtain the deviation rate of a virtual machine resource bwteen performance parameters and average performance value; and finally the deviation rate is stored as the virtual machine resource performance score.(4) Firstly,calculate the Mahalanobis distance between task preferences and resource performance index score with the Mahalanobis distance formula.And then calculates energy consumption.Finally,calculate index value depending on the type of task. In the scheduling process, select the virtual machine of the minimum index value resources for the tasks, and use the value of the Mahalanobis distance as a measure of user satisfaction task.(5) Finally, by extending Cloudlet class, Vm class and DataCenterBroker class in CloudSim so that the proposed energy-aware scheduling strategy can be implement; CloudSim platform is compiled.Experimental results show that scheduling strategy proposed in this paper can improve the efficiency of task execution and user tasks satisfaction, and reduce energy consumption in the environment.