Research On Cloud Resource Management Technology Based On Serverless Architecture

At present,under the background of big data,the Internet of things,artificial intelligence and cloud computing,cloud computing is the foundation of everything.The Internet of things,big data and artificial intelligence must rely on cloud computing to play their maximum role.Cloud computing uses virtualization technology to provide users with a new type of computing model with high scalability.However,the low utilization of cluster resources becomes the primary problem faced by the data center.Serverless architecture,as a subversion of application development,has become a new favorite of the times.For enterprises,it is unnecessary to provide and maintain servers,and reserve capacity for services such as calculation and storage,so as to reduce deployment cost and operation cost for the company.Its most significant feature is that it scales on demand and charges for use.From the perspective of cloud service providers,the widespread application of serverless architectures requires effective optimization and management of cloud resources to reduce operating costs.Therefore,how to effectively manage data center resources to reduce energy consumption,improve resource utilization and reduce operating costs is particularly important.The main research work of this paper includes the following two aspects:(1)Analyze the energy consumption problem of the data center,describe the energy consumption composition and build an energy consumption model,based on the idea of queuing theory to solve the optimal value of the standby server opening and closing thresholds,and establish a mathematical model to determine the energy consumption and the threshold,the average waiting time The relationship between performance and energy consumption is given.(2)A cloud data center deployment and scheduling model and a virtual machine deployment and scheduling strategy based on ant colony optimization are proposed.Compared with the basic ant colony,the ant colony optimization algorithm mainly reflects the optimization of heuristic factors,optimization of consumption state,and optimization of pheromone concentration update.Then,when the ant colony optimization algorithm is used to solve the problem of virtual machine deployment and scheduling,the optimization model and algorithm flow are explained in detail.The ant colony optimization algorithm(MACO)is compared with the polling algorithm(RR),the Min-Min algorithm,and the basic ant colony algorithm(BACO).The tasks are completed from task completion time,comparison experiment,platform occupation time,and system energy consumption.In terms of verification,the experimental results show that the ant colony optimization algorithm performs well in terms of task completion time,resource utilization,service stability,and reduction of energy consumption.The above research will lay the foundation for subsequent research on cloud resource management technologies,so that virtual machine deployment and scheduling algorithms can be better applied to large-scale,complex load,and diverse resource cloud data centers.