您的位置:山东大学 -> 科技期刊社 -> 《山东大学学报(工学版)》

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"

1 TOOSI A N , BUYYA R . Virtual networking with azure for hybrid cloud computing in aneka[J]. Research Advances in Cloud Computing, 2017, 93- 114.
2 KOZHIRBAYEV Z , SINNOTT R O . A performance comparison of container-based technologies for the cloud[J]. Future Generation Computer Systems, 2017, 68, 175- 182.
3 LI Y, ZHANG J, ZHANG W, et al. Cluster resource adjustment based on an improved artificial fish swarm algorithm in mesos[C]//IEEE International Conference on Signal Processing. Washington D C, USA: IEEE Computer Society, 2016: 1843-1847.
4 吴龙辉. Kubernetes实战[M]. 北京: 电子工业出版社, 2016: 2- 9.
5 崔广章, 朱志祥. 容器云资源调度策略的改进[J]. 计算机与数字工程, 2017, 45 (10): 1931- 1936.
CUI Guangzhang , ZHU Zhixiang . Improved container cloud resource scheduling policy[J]. Computer & Digital Engineering, 2017, 45 (10): 1931- 1936.
6 唐瑞.基于Kubernetes的容器云平台资源调度策略研究[D].成都:电子科技大学, 2017.
TANG Rui. Research on resources scheduling strategy of container cloud platform based on kubernetes[D]. Chengdu: University of Electronic Science and Technology of China, 2017.
7 杜威科.基于Kubemetes的大数据流式计算Spark平台设计与实现[D].南京:南京邮电大学, 2017.
DU Weike. Design and implementation of spark platformfor big data atreaming computing based on kubernetes[D]. Nanjing: Nanjing University of Posts and Telecommunications, 2017.
8 柯尊旺, 于炯, 廖彬. 适应异构集群的Mesos多资源调度DRF增强算法[J]. 计算机应用, 2016, 36 (5): 1216- 1221.
KE Zunwang , YU Jiong , LIAO Bin . DRF enhanced algorithm for mesos multi resource scheduling adapted to heterogeneous clusters[J]. Computer Application, 2016, 36 (5): 1216- 1221.
9 冯兴杰, 贺阳. 基于节点性能的Hadoop作业调度算法改进[J]. 计算机应用与软件, 2017, (5): 223- 228.
FENG Xingjie , HE Yang . Improvement of scheduling algorithm on hadoop based on node performance[J]. Computer Applications and Software, 2017, (5): 223- 228.
10 杨晨.面向高性能计算的YARN平台关键技术与应用研究[D].南京:南京大学, 2016.
YANG Chen. Research on key technologies and application on yarn for high-performance computing[D]. Nanjing: Nanjin University, 2016.
11 魏赟, 陈元元. 基于改进蚁群算法的云计算任务调度模型[J]. 计算机工程, 2015, 41 (2): 12- 16.
WEI Yun , CHEN Yuanyuan . Cloud computing task scheduling model based on improved ant colony algorithm[J]. Computer Engineering, 2015, 41 (2): 12- 16.
12 CHO K M , TSAI P W , TSAI C W , et al. A hybrid meta-heuristic algorithm for vm scheduling with load balancing in cloud computing[J]. Neural Computing & Applications, 2015, 26 (6): 1- 13.
13 王永贵, 韩瑞莲. 基于改进蚁群算法的云环境任务调度研究[J]. 计算机测量与控制, 2011, 19 (5): 1203- 1205.
WANG Yonggui , HAN Ruilian . Study on cloud computing task schedule strategy based on maco algorithm[J]. Computer Measurement & Control, 2011, 19 (5): 1203- 1205.
14 张爱科, 谢翠兰. 基于公平性和负载均衡的云计算任务调度算法[J]. 计算机应用与软件, 2015, (2): 268- 271.
ZHANG Aike , XIE Cuilan . Task scheduling algorithm in cloud computing based on fairness and load balancing[J]. Computer Applications & Software, 2015, (2): 268- 271.
15 FANG Y, WANG F, GE J. A task scheduling algorithm based on load balancing in cloud computing[C]//International Conference on Web Information Systems and Mining. Berlin, Germany: Springer-Verlag, 2010: 271-277.
16 LIU Wanjuna , ZHANG Menghuab , GUO Wenyueb . Cloud computing resource schedule strategy based on mpsoalgorithm[J]. Computer Engineering, 2011, 37 (11): 42- 43.
17 YANG X, CHEN T, ZHANG Q. Research on cloud computing schedule based on improved hybrid PSO[C]//International Conference on Computer Science and Network Technology. Washington D C, USA: IEEE Computer Society, 2014: 388-391.
18 XIONG Y Y , WU Y Y . Cloud computing resource schedule strategy based on pso algorithm[J]. Applied Mechanics & Materials, 2014, 513-517, 1332- 1336.
19 LIU X , ZHANG X , LI W , et al. Swarm optimization algorithms applied to multi-resource fair allocation in heterogeneous cloud computing systems[J]. Computing, 2017, 99 (12): 1231- 1255.
20 WANG W , LIANG B , LI B . Multi-resource fair allocation in heterogeneous cloud computing systems[J]. IEEE Transactions on Parallel & Distributed Systems, 2015, 26 (10): 2822- 2835.
[1] 邵孟伟,袁世飞,周宏志,王乃华. 基于BP神经网络和遗传算法的翅片管结构优化[J]. 山东大学学报 (工学版), 2025, 55(6): 76-82.
[2] 王瑞琪,刘继彦,鞠文杰,王为帅,许文泽,张祯滨. 考虑混合储能的电-氢系统日前-日内协同优化调度[J]. 山东大学学报 (工学版), 2025, 55(2): 28-36.
[3] 杜睿山,井远光,孟令东,张豪鹏. 基于改进多目标粒子群算法的储气库注气优化[J]. 山东大学学报 (工学版), 2024, 54(4): 42-50.
[4] 宋修广,郭鑫铭,闫方,李国强,田源. 公路应急救援车辆智能调度技术[J]. 山东大学学报 (工学版), 2023, 53(4): 1-17.
[5] 余明骏,刁红军,凌兴宏. 基于轨迹掩膜的在线多目标跟踪方法[J]. 山东大学学报 (工学版), 2023, 53(2): 61-69.
[6] 韩学山, 李克强. 适应新型电力系统发展的协同调度理论研究[J]. 山东大学学报 (工学版), 2022, 52(5): 14-23.
[7] 孙东磊,杨思,韩学山,叶平峰,王宪,刘蕊. 高比例风电接入下计及时段间耦合旋转备用响应风险的动态经济调度方法[J]. 山东大学学报 (工学版), 2022, 52(5): 111-122.
[8] 孙东磊,孙可奇,杨金叶,曹永吉,袁振华,刘冬,张恒旭. 考虑灵活性需求的电力系统优化调度[J]. 山东大学学报 (工学版), 2022, 52(1): 120-127.
[9] 赵康,田浩,马欢,杨冬. 基于复杂网络理论的多直流馈入受端电网优化分区方法[J]. 山东大学学报 (工学版), 2022, 52(1): 128-134.
[10] 黄澄,袁东风,张海霞. 基于狮群算法的数字孪生车间调度问题优化[J]. 山东大学学报 (工学版), 2021, 51(4): 17-23.
[11] 宗欣露,杜佳圆. 基于多目标驱动人工蜂群算法的疏散仿真模型[J]. 山东大学学报 (工学版), 2021, 51(3): 1-6.
[12] 武慧虹,钱淑渠,刘衍民,徐国峰,郭本华. 精英克隆局部搜索的多目标动态环境经济调度差分进化算法[J]. 山东大学学报 (工学版), 2021, 51(1): 11-23.
[13] 李德鑫, 宗崇林, 王佳蕊, 张海锋, 刘畅, 黄大为. 计及特高压直流外送及转运合同约束的日前优化调度[J]. 山东大学学报 (工学版), 2021, 51(1): 69-75.
[14] 潘志远, 刘超男, 李宏伟, 王婧, 王威, 刘静, 郑鑫. 基于分时电价的含光伏的智慧家庭能量调度方法[J]. 山东大学学报 (工学版), 2020, 50(3): 111-116.
[15] 孙润稼,朱海南,刘玉田. 基于偏好多目标优化和遗传算法的输电网架重构[J]. 山东大学学报 (工学版), 2019, 49(5): 17-23.
Viewed
Full text


Abstract

Cited

  Shared   
  Discussed   
[1] 李 侃 . 嵌入式相贯线焊接控制系统开发与实现[J]. 山东大学学报(工学版), 2008, 38(4): 37 -41 .
[2] 来翔 . 用胞映射方法讨论一类MKdV方程[J]. 山东大学学报(工学版), 2006, 36(1): 87 -92 .
[3] 余嘉元1 , 田金亭1 , 朱强忠2 . 计算智能在心理学中的应用[J]. 山东大学学报(工学版), 2009, 39(1): 1 -5 .
[4] 陈瑞,李红伟,田靖. 磁极数对径向磁轴承承载力的影响[J]. 山东大学学报(工学版), 2018, 48(2): 81 -85 .
[5] 王波,王宁生 . 机电装配体拆卸序列的自动生成及组合优化[J]. 山东大学学报(工学版), 2006, 36(2): 52 -57 .
[6] 张英,郎咏梅,赵玉晓,张鉴达,乔鹏,李善评 . 由EGSB厌氧颗粒污泥培养好氧颗粒污泥的工艺探讨[J]. 山东大学学报(工学版), 2006, 36(4): 56 -59 .
[7] Yue Khing Toh1 , XIAO Wendong2 , XIE Lihua1 . 基于无线传感器网络的分散目标跟踪:实际测试平台的开发应用(英文)[J]. 山东大学学报(工学版), 2009, 39(1): 50 -56 .
[8] 刘忠国,张晓静,刘伯强,刘常春 . 视觉刺激间隔对大脑诱发电位的影响[J]. 山东大学学报(工学版), 2006, 36(3): 34 -38 .
[9] 孙炜伟,王玉振. 考虑饱和的发电机单机无穷大系统有限增益镇定[J]. 山东大学学报(工学版), 2009, 39(1): 69 -76 .
[10] 孙玉利,李法德,左敦稳,戚美 . 直立分室式流体连续通电加热系统的升温特性[J]. 山东大学学报(工学版), 2006, 36(6): 19 -23 .