Research On Data Center Resource Scheduling In Cloud Network Convergence Environment

With the continuous development of cloud computing and Internet technologies,and the deepening of cloud-network integration,the data center resources have become heterogeneous.Due to the complexity and variety of application service types and user needs,the load in the cloud environment also shows diversity.How to reasonably distribute the load in a heterogeneous environment and improve the resource scheduling efficiency of cloud data centers is of great significance.In order to achieve data center load balancing,this paper first analyzes the load characteristics of cloud computing data centers and establishes a load assessment model;then studies data center resource scheduling from two aspects:task allocation and virtual machine resource management.Finally,use the CloudSim cloud simulation platform for experimental analysis.The main content of the paper is as follows:(1)By analyzing the characteristics of cloud load,a load assessment scheme based on dynamic threshold is proposed.Based on the analysis of resource allocation and usage in the clusterdata dataset released by Alibaba Cloud in 2018,a four-dimensional load index was selected to establish a load model.Aiming at the problem of low flexibility in judging the load status by the static threshold method,a dynamic threshold-based load evaluation scheme is proposed to comprehensively consider the global load and the proportion of high and low nodes to determine the load status of the nodes,which lays the foundation for resource scheduling.Simulation experiments prove that the node load evaluation method with better flexibility and adaptability can effectively evaluate the node load,and actively promotes resource scheduling.It can effectively maintain data center load and reduce virtual machine migration frequency.(2)In task allocation,a workflow scheduling strategy based on genetic bee colony hybrid algorithm is proposed.For the scheduling problem of associated workflow tasks,comprehensively considering the load situation of the data center and the availability of heterogeneous resources such as nodes and virtual machines,the scheduling objective optimization function is determined.Then a workflow task scheduling strategy based on the combination of genetic algorithm and bee colony algorithm is designed.For the problem of slow optimization of the artificial bee colony algorithm in the early stage,this strategy uses genetic algorithm to optimize the bee colony algorithm,and uses the elite saving strategy to improve the individual quality of the population and the task allocation efficiency.Simulation experiments prove that the strategy is effective in optimizing the completion time of the workflow and improving the data center load balancing problem.(3)In terms of virtual machine resource management,a resource scheduling strategy based on dynamic integration of virtual machines is proposed.Aiming at the phenomenon that the load of individual nodes is too high or too low due to the uneven utilization of heterogeneous node resources in the data center,a virtual machine integration solution based on virtual machine dynamic migration technology is used to re-allocate the resources of the nodes with excessively high and low loads.Based on the dynamic threshold-based load assessment,the virtual machines to be migrated are selected based on the dependency of virtual machine and node resources and the contribution of the virtual machine,and the destination node is selected from the perspective of minimum communication cost and load balancing,and virtual machine migration is performed to realize dynamic adjustment of virtual machine resources of the node.Experiments show that this solution can effectively reduce the number of virtual machine migrations,improve node resource utilization and ensure data center service quality,thereby optimizing node resource allocation and improving data center load balancing.