计算机应用 ›› 2018, Vol. 38 ›› Issue (5): 1399-1403.DOI: 10.11772/j.issn.1001-9081.2017102789

• 先进计算 • 上一篇    下一篇

基于Docker swarm集群的动态加权调度策略

黄凯1, 孟庆永1, 谢雨来1,2, 冯丹1,2, 秦磊华1,2   

  1. 1. 华中科技大学 计算机科学与技术学院, 武汉 430074;
    2. 武汉国家光电实验室(华中科技大学), 武汉 430074
  • 收稿日期:2017-11-27 修回日期:2017-11-27 出版日期:2018-05-10 发布日期:2018-05-24
  • 通讯作者: 谢雨来
  • 作者简介:黄凯(1993-),男,湖北武汉人,硕士研究生,CCF会员,主要研究方向:Docker容器、虚拟化、集群调度;孟庆永(1995-),男,河南南阳人,主要研究方向:Docker容器、集群调度;谢雨来(1984-),男,湖北潜江人,副教授,博士,CCF会员,主要研究方向:溯源系统、网络存储、计算机系统结构;冯丹(1970-),女,湖北荆门人,教授,博士,CCF杰出会员,主要研究方向:计算机系统结构、海量存储系统、并行文件系统、磁盘阵列、固态盘;秦磊华(1968-),男,湖北鄂州人,教授,博士,CCF会员,主要研究方向:网络存储、网络仿真、溯源系统。
  • 基金资助:
    国家自然科学基金资助项目(61402189);CCF-启明星辰鸿雁基金资助项目(2016-015);武汉市应用基础研究计划项目(2017010201010104)。

Dynamic weighted scheduling strategy based on Docker swarm cluster

HUANG Kai1, MENG Qingyong1, XIE Yulai1,2, FENG Dan1,2, QIN Leihua1,2   

  1. 1. College of Computer Science and Technology, Huazhong University of Science and Technology, Wuhan Hebei 430074, China;
    2. Wuhan National Laboratory for Optoelectronics(Huazhong University of Science and Technology), Wuhan Hebei 430074, China
  • Received:2017-11-27 Revised:2017-11-27 Online:2018-05-10 Published:2018-05-24
  • Contact: 谢雨来
  • Supported by:
    This work is partially supported by the National Natural Science Foundation of China (61402189), the CCF-Venustech Hongyan Research Fund (2016-015), the Wuhan Municipal Applied Basic Research Program (2017010201010104).

摘要: 针对目前的Docker swarm内置的调度策略无法很好地实现Docker集群的负载均衡并且对集群资源的使用率不高的问题,提出了一种动态加权调度算法。所提算法对资源设置权重系数,引入参数bias针对不同服务对资源权重进行动态调整,根据各个节点的实际资源利用情况,对节点资源按照权重进行加权计算,用权值反映节点负载,并将此作为调度依据。在和Docker原始调度策略以及无参数调整的加权调度策略的对比实验中,该算法使得集群中各个节点上的各项资源利用率更加均衡;同时,在集群负载比较高的情况下,该算法实现了更快的服务运行速度。

关键词: Docker, swarm, 集群, 权值, 调度策略

Abstract: As the built-in scheduling strategy of Docker swarm cannot implement load balance of cluster very well and the utilization rate of cluster resource is not very high, a dynamic weighted scheduling algorithm was proposed. The weight coefficient was set on the resource, and the parameter bias was introduced to dynamically adjust the resource weight for different services. According to the actual resource utilization of each node, the node weight was calculated to reflect node load, and was used for scheduling. Compared with the original Docker scheduling strategy and the weighted scheduling strategy without parameter adjustment, the proposed algorithm makes all the resource utilization of each node in the cluster more balanced. At the same time, the proposed algorithm can achieve faster service running speed under the condition of high cluster load.

Key words: Docker, swarm, cluster, weight, scheduling strategy

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