Research On Data Center Resource Allocation Mechanism

In recent years,the rapid development of cloud computing has attracted the attention of academia and industry.As a new business model and resource acquisition method,cloud computing has changed people's lifestyle and network service forms.As the scale of the cloud computing industry increases year by year,the scale of the data center is also expanding,and it is evolving toward large-scale and globalized.The ever-expanding scale of cloud computing data centers brings new problems and challenges to resource allocation and management in data centers.The energy consumption of data centers increases sharply with the expansion of resources due to the unreasonable allocation of resources,thereby increasing the operating costs of data centers and reducing the economic benefits of data centers.In addition,data center resource utilization is generally low,which is also one of the important reasons for reducing the economic benefits of data centers.Secondly,there is often an imbalance in resource allocation in the data center.Unbalanced load leads to excessive virtual machine migration and consumes network resources inside the data center.The imbalance in the use of internal resources of the server leads to waste of resources.In addition,the high resource utilization of the data center physical server will also reduce the performance of the data center.Excessive load will destroy the isolation of the virtual machine,thereby increasing resource contention and reducing performance.In view of the above problems,this paper presents a multi-objective optimization improved cuckoo search algorithm to solve the problem of data center resource allocation.Specifically,this paper first analyzes the background and objectives of the resource allocation problem in the data center,and proposes the energy cost model,the system equilibrium cost model and the resource contention cost model.The energy consumption model comprehensively considers the energy consumption of the physical server and the network switch,and the system balance cost comprehensively considers the load balancing between servers and the imbalance of internal resource allocation of the server.And give the quantitative expression of the three.And the problem is modeled as a multi-objective optimization packing problem.The improved cuckoo search algorithm is used to solve the packing problem.There are two improvements.One is to reduce the complexity of the algorithm.By analyzing the optimization direction of the data center,the three cost functions are weighted and merged into a single-objective optimization problem.Secondly,in the process of searching for the optimal solution,in order to avoid the common local optimal problem of heuristic algorithm,the artificial interference factor is added based on Cauchy mutation to avoid the algorithm falling into local optimum and improve the performance of the algorithm.Through simulation analysis,the improved cuckoo search algorithm given by this standard can well balance multiple optimization goals,and has better convergence and time complexity,which can improve the overall performance of the cloud computing data center.