Aiming at the problem of flexible scheduling of private cloud resources, Kubernetes combined with the existing Openstack cloud platform, we propose a flexible scheduling strategy based on container.On the one hand, because Openstack virtual machine takes a long time to start up and brings extra time cost to scheduling, Docker container is used to replace the default virtual machine of Openstack by taking advantage of the feature that container takes much less time to pull up than virtual machine.On the one hand, the Kubernetes scheduling algorithm is optimized, and an optimization model to improve the utilization rate of cluster resources is established.By monitoring four types of resources of each server node of the cloud platform and applying queue preset template matching, the server with the highest utilization rate of scheduling resources is selected.The whole scheduling process includes the initial scheduling of container applications and the online migration algorithm.Experiment shows that compared with the original Kubernetes scheduling algorithm and some other scheduling strategies, this scheduling strategy divides the data center resources into finer granularity.While ensuring the server performance, it realizes the flexible scheduling of cloud platform resources, improves the utilization rate of cluster resources and reduces the energy consumption of the data center.%针对私有云资源弹性调度问题, 将Kubernetes结合已有Openstack云平台, 提出一种基于容器的弹性调度策略.一方面, 因为Openstack虚拟机启动时间较长, 给调度带来额外时间开销, 所以利用容器拉起时耗远小于虚拟机的特性, 用Docker容器取代了Openstack默认的虚拟机;一方面优化了Kubernetes调度算法, 建立了一个提高集群资源利用率的优化模型, 通过对云平台各个服务器节点四种类型资源的监控和应用队列预设模板匹配, 选择调度资源利用率最高的服务器.整个调度过程包括容器应用的初次调度和在线迁移算法.实验结果表明, 相比原有Kubernetes调度算法和一些其他的调度策略, 该调度策略对数据中心资源进行了更细粒度的划分, 在保证服务器性能的同时, 实现了云平台资源弹性调度, 集群资源利用率也得到了提高, 同时降低了数据中心能耗.
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