一种基于多目标的容器云任务调度算法

doi:10.6040/j.issn.1672-3961.0.2018.210

摘要/Abstract

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

谢晓兰,王琦. 一种基于多目标的容器云任务调度算法[J]. 山东大学学报 (工学版), 2020, 50(4): 14-21.

Xiaolan XIE,Qi WANG. A scheduling algorithm based on multi-objective container cloud task[J]. Journal of Shandong University(Engineering Science), 2020, 50(4): 14-21.

图/表 7

图1

图2

图3

图4

图5

图6

图7

参考文献 20

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.

相关文章 15

[1]	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, 124.
[2]	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.
[3]	杨冬,王世文,王勇,陈博,郑天茹,周宁,肖天,赵雅文. 并网型风电场扩展光伏互补发电容量优化配置[J]. 山东大学学报 (工学版), 2019, 49(5): 44-51.
[4]	张中伟,梅红岩,周军,贾慧萍. 基于多目标协同进化遗传算法的规则提取方法[J]. 山东大学学报 (工学版), 2019, 49(2): 122-130.
[5]	刘洪铭,曾鸿雁,周伟,王涛. 基于改进粒子群算法作业车间调度问题的优化[J]. 山东大学学报 (工学版), 2019, 49(1): 75-82.
[6]	黄劲潮. 基于快速区域建议网络的图像多目标分割算法[J]. 山东大学学报(工学版), 2018, 48(4): 20-26.
[7]	钱淑渠,武慧虹,徐国峰,金晶亮. 计及排放的动态经济调度免疫克隆演化算法[J]. 山东大学学报(工学版), 2018, 48(4): 1-9.
[8]	梁志远,龚庆武,陈元峰. 电热水器与变频空调负荷群的联合调度控制[J]. 山东大学学报(工学版), 2018, 48(2): 100-106.
[9]	宋正强,杨辉玲,肖丹. 基于在线粒子群优化方法的IPMSM驱动电流和速度控制器[J]. 山东大学学报(工学版), 2018, 48(1): 112-116.
[10]	王士柏,杜恒,武勇,刘洪正,程艳. 两级市场运行环境下微电网经济调度[J]. 山东大学学报(工学版), 2017, 47(6): 32-38.
[11]	褚晓东,唐茂森,高旭,刘伟生,贾善杰,李笋. 基于集中式信息系统的主动配电网鲁棒优化调度[J]. 山东大学学报(工学版), 2017, 47(6): 20-25.
[12]	王飞,徐健,李伟,汪新浩,施啸寒. 基于分布式储能系统的风储滚动优化调度方法[J]. 山东大学学报(工学版), 2017, 47(6): 89-94.
[13]	裴小兵,陈慧芬,张百栈,陈孟辉. 改善式BVEDA求解多目标调度问题[J]. 山东大学学报(工学版), 2017, 47(4): 25-30.
[14]	马帅依凡,赵子健. 基于人工标记的手术导航仪[J]. 山东大学学报(工学版), 2017, 47(3): 63-68.
[15]	邓冠龙,杨洪勇,张淑宁,顾幸生. 零等待flow shop多目标调度的混合差分进化算法[J]. 山东大学学报(工学版), 2016, 46(5): 21-28.

多维度评价

Viewed

Full text

573

HTML			PDF

Just accepted	Online first	Issue	Just accepted	Online first	Issue
0	0	176	0	0	397

From	Others	local

Times	36	537
Rate	6%	94%

Abstract

1382

Just accepted	Online first	Issue

0	0	1382

From	Others	local

Times	1381	1
Rate	100%	0%

Cited

Web of Science	Crossref	ScienceDirect	Search for Citations in Google Scholar >>


This page requires you have already subscribed to WoS.

Shared

Discussed