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山东大学学报 (工学版) ›› 2020, Vol. 50 ›› Issue (4): 14-21.doi: 10.6040/j.issn.1672-3961.0.2018.210

• 机器学习与数据挖掘 • 上一篇    下一篇

一种基于多目标的容器云任务调度算法

谢晓兰(),王琦*()   

  1. 桂林理工大学信息科学与工程学院, 广西 桂林 541006
  • 收稿日期:2018-05-25 出版日期:2020-08-20 发布日期:2020-08-13
  • 通讯作者: 王琦 E-mail:237290696@qq.com;hanbingxzy@gmail.com
  • 作者简介:谢晓兰(1974—),女,广西桂林人,教授,博士,主要研究方向为云计算和大数据. E-mail:237290696@qq.com
  • 基金资助:
    2017年度国家自然科学基金(61762031);广西创新驱动重大专项(桂科AA19046004);广西“嵌入式技术与智能系统”重点实验室主任基金(2018A-03);广西研究生教育创新计划项目(YCSW2017156)

A scheduling algorithm based on multi-objective container cloud task

Xiaolan XIE(),Qi WANG*()   

  1. College of Information Science and Engineering, Guilin University of Technology, Guilin 541006, Guangxi, China
  • Received:2018-05-25 Online:2020-08-20 Published:2020-08-13
  • Contact: Qi WANG E-mail:237290696@qq.com;hanbingxzy@gmail.com

摘要:

为解决容器云调度模型面向同构任务、同构资源和单一目标造成的不实用、不公平、不高效、不均衡问题,提出带约束修复的树形调度目标模型,基于异构任务和异构资源,采用约束修复避免映射方案不可行,采用优先级综合多个子目标并将其归属于不同树形分支下的子空间,最终实现多个上层应用框架之间的公平、高效、节约、均衡调度模型。试验结果表明,带约束修复的树形调度目标模型在公平度上不比其它对比单目标模型差,可满足更多的任务的需求,并在此前提下拥有更高的资源利用率和负载均衡度,在实用性、公平性、高效与均衡上优于单目标模型,可有效保证公平分配资源,增加容器服务收益,降低物理资源成本,提高稳定性和可用性。

关键词: 容器云, 调度, 智能控制, 多目标

Abstract:

In order to solve the unrealistic, unfair, inefficient and unbalanced problems caused by container cloud scheduling model facing isomorphic tasks, isomorphic resources and single objectives, a tree scheduling objective model with constraint repair was proposed. Based on heterogeneous tasks and resources, constraint repair was adopted to avoid the impracticability of mapping scheme, and then priority to synthesize multiple sub-goals and attributed them to sub-spaces under different tree branches, and eventually achieved a fair, efficient, economical and balanced scheduling model among multiple upper application frameworks. The experimental results showed that the tree scheduling objective model with constrained repair was not inferior to other single-objective models in fairness, which could meet more tasks, and had higher resource utilization and load balancing under the preceding conditions. It was superior to the single-objective model in practicability, fairness, efficiency and balancing and ensured fair allocation of resources, which increased the benefits of container services, decreased the cost of physical resources, increased the stability and availability.

Key words: container cloud, scheduling, intelligent control, multi-object

中图分类号: 

  • TP391

图1

容器云与相关服务层次关系"

图2

资源分配状态迁移"

图3

估价树"

图4

公平度对比"

图5

资源需求满足量对比"

图6

资源利用率对比"

图7

负载均衡度对比"

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