[1]张可颖,彭丽苹,吕晓丹,等.开源云上的 Kubernetes弹性调度[J].计算机技术与发展,2019,29(02):109-114.[doi:10．3969/j．issn．1673－629X．2019．02．023]
　ZHANG Keying,PENG Liping,LYU Xiaodan,et al.Elastic Scheduling Strategy for Private Cloud Resource Based on Kubernetes and Openstack[J].,2019,29(02):109-114.[doi:10．3969/j．issn．1673－629X．2019．02．023]
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开源云上的 Kubernetes弹性调度()
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《计算机技术与发展》[ISSN:1006-6977/CN:61-1281/TN]

卷:
29
期数:
2019年02期
页码:
109-114
栏目:
智能、算法、系统工程
出版日期:
2019-02-10

文章信息/Info

Title:
Elastic Scheduling Strategy for Private Cloud Resource Based on Kubernetes and Openstack
文章编号:
1673－629X(2019)02－0109－06
作者:
张可颖1; 彭丽苹2; 吕晓丹2; 吕尚青3
1．贵州大学 大数据与信息工程学院，贵州 贵阳 550025;2．贵州大学 计算机科学与技术学院，贵州 贵阳 550025;3．北京邮电大学 信息与通信工程学院，北京 100000
Author(s):
ZHANG Ke－ying1 ; PENG Li－ping2 ; LYU Xiao－dan2 ; LYU Shang－qing3
1．School of Big Data and Information Engineering，Guizhou University，Guiyang 550025，China;2．School of Computer Science and Technology，Guizhou University，Guiyang 550025，China;3．School of Information and Communication Engineering，Beijing University of Post
关键词:
私有云; 弹性调度; Kubernetes; 容器; Openstack
Keywords:
private cloud; elastic scheduling; Kubernetes; container; Openstack
分类号:
TP319
DOI:
10．3969/j．issn．1673－629X．2019．02．023
摘要:
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。
Abstract:
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．

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更新日期/Last Update: 2019-02-10