Research On Energy Consumption Optimization Strategy For Green Cloud Computing

In recent years,cloud computing has grown rapidly on a global scale and in particular,driven by Google,Microsoft,Amazon and other world-renowned IT companies,cloud computing has been widely promoted.With the development of cloud computing,its own energy consumption can not be ignored,and the impact on the environment is becoming more and more prominent,so the problem of energy consumption is becoming more and more concerned.In today's era of advocating green development,saving resources and protecting the environment is the theme of the times,so energy consumption has become a transparent obstacle to the development of cloud computing,and to reduce the amount of energy through energy optimization strategy is an important foundation to realize the green cloud computing system.In the backgroud of green cloud computing,this paper presents two energy optimization strategies for cloud computing.One is based on virtual machine placement problem,combining approximation algorithm and genetic algorithm,a hybrid genetic algorithm is proposed.The algorithm uses best fit decreasing algorithm for local search on the basis of genetic algorithm,and then apply it to the virtual machine placement problem,and the energy-saving virtual machine placement strategy is generated to achieve energy optimization.Another energy optimization strategy is based on the task scheduling,in order to reduce the energy consumption and the execution time of the task,a dynamic fusion task scheduling algorithm is proposed by combining genetic algorithm with ant colony algorithm,and its goal is to reduce the energy consumption of cloud computing data centers.In this thesis,using CloudSim as a cloud computing simulation platform,the virtual machine placement and the task scheduling algorithm are verified to be effective from different aspects,and two kinds of energy optimization strategy has been successfully applied in the e-commerce industry.