摘要
在云计算环境中,随着容器数量和类型的不断增加,资源管理和调度复杂性也增加,如何有效地调度容器,优化资源利用率和集群性能成为一个重要的研究课题。现有的容器集群调度策略没有充分考虑容器的多样化需求,缺乏灵活性,难以实现针对不同场景的容器进行定制化调度,容易出现集群资源利用率低下、集群资源负载失衡等问题。为了满足容器多样化需求,提高集群资源负载均衡性,提出了一种基于GMM(高斯混合模型)的容器定制化调度策略(Customized Container Scheduling Strategy Based on GMM,CS-GMM)。首先,根据容器的资源和属性需求进行分类,将其划分为不同的类型。其次,对于每一类容器,分别计算并分配不同的独立权重,依次将容器根据其类型调度到合适的节点,从而实现定制化调度。通过这种方式,满足了容器多样化需求,使不同类型的容器可以根据其特定需求得到最优的资源配置,避免了资源竞争和冲突,从而提高了集群资源的整体利用率和负载均衡度。实验结果表明,与Kubernetes Scheduler相比,该调度策略在多种容器调度场景下均表现出优越的性能,集群节点之间最大资源利用率差值降低17.1%,容器调度成功率提升19%,集群节点负载均衡度提升57.51%。
In cloud computing environments,as the number and types of containers continue to increase,resource management and scheduling complexity are increased.How to effectively schedule containers and optimize resource utilization and cluster performance has become an important research topic.The existing container cluster scheduling strategies do not fully consider the diverse needs of containers,lack flexibility,and are difficult to customize scheduling for containers in different scenarios.This can easily lead to problems such as low cluster resource utilization and imbalanced cluster resource load.In order to meet the diverse needs of containers and improve the load balancing of cluster resources,this paper proposes a customized container scheduling strategy based on GMM(Gaussian Mixture Model).Firstly,classify according to the resources and attribute requirements of the container,and divide it into different types.Secondly,for each type of container,different independent weights are calculated and assigned separately,and the containers are scheduled to appropriate nodes according to their types in turn,thereby achieving customized scheduling.In this way,the diverse needs of containers are met,so that different types of containers can get the optimal resource allocation according to their specific needs,avoiding resource competition and conflicts,thereby improving the overall utilization and load balancing of cluster resources.Experimental results show that compared with Kubernetes Scheduler,this scheduling strategy has shown superior performance in various container scheduling scenarios,with the maximum resource utilization difference between cluster nodes reduced by 17.1%,the container scheduling success rate increased by 19%,and the cluster node load balancing increased by 57.51%.
作者
周凯
王凯
朱宇航
普黎明
刘树新
周德强
ZHOU Kai;WANG Kai;ZHU Yuhang;PU Liming;LIU Shuxin;ZHOU Deqiang(National Digital Switching System Engineering&Technological R&D Center,Information Engineering University,Zhengzhou 450003,China)
出处
《计算机科学》
北大核心
2025年第6期346-354,共9页
Computer Science
基金
河南省重大科技专项(221100210700-2)。
关键词
云计算
容器调度
多样化
定制化
负载均衡
Cloud computing
Container scheduling
Diversification
Customization
Load balancing
