Research On Energy Consumption Optimization And Resource Provisioning Method In Cloud Data Center

Along with the development of distributed computing,grid computing,utility computing technology,the researchers compromise the advantages of these traditional computer science and network technology,and present a more advanced computing method--cloud computing,which is a great change in the field of the internet.However,with the wide deployment of cloud computing data centers,the problem of power consumption has become increasingly prominent.The problem of high energy consumption in the cloud data centers are started,and then energy management strategy and task scheduling algorithm are designed.It improves the efficiency of cloud computing and reduces energy consumption,and brings benefits to the data center operators.The dynamic energy management problem in pursuit of energy-efficiency in cloud datacenters is investigated.Specifically,a dynamic energy management system model for cloud datacenters is built.According to Task Scheduling Module,the scheduling process is analyzed by Stochastic Petri Net,and a task-oriented resource allocation method(LET-ACO)is proposed,which optimizes the running time of the system and the energy consumption by scheduling tasks.The main contents of this paper are as follows:A dynamic energy management system model for cloud datacenters is built,and this system is composed of DVS Management Module,Load Balancing Module and Task Scheduling Module.The task execution time prediction model and energy consumption prediction model are built by analyzing the characteristics of the virtual environment and the energy consumption of cluster cloud data center.Based on MapReduce execution process,the corresponding elements and events in the process are presented by Petri net,the cloud computing task schedulingprocessing refinement model is established.And the performance analysis method of the model is given,finally,the performance parameters of the system are obtained,which provides the necessary model foundation and analysis basis for the design task scheduling strategy.Based on the task scheduling process model designed in this paper,the ant colony algorithm is selected used to find the entry point of the combination of genetic algorithm and task scheduling process.The task scheduling strategy based on ant colony algorithm is designed.After theoretical analysis,model construction,strategy designing and implementation,the final experimental results shows that the proposed virtual machine-based task scheduling strategy can shorten the total time of task completion and reduce the total energy consumption of all tasks.The validity of the task scheduling strategy proposed by the topic.