基于进化多目标优化的微服务组合部署与调度策略

doi:10.3969/j.issn.1001-506X.2020.01.13

系统工程与电子技术 ›› 2020, Vol. 42 ›› Issue (1): 90-100.doi: 10.3969/j.issn.1001-506X.2020.01.13

基于进化多目标优化的微服务组合部署与调度策略

马武彬^1,²(), 王锐¹(), 王威超²(), 吴亚辉^1,*(), 邓苏¹(), 黄宏斌¹()

1. 国防科技大学信息系统工程重点实验室, 湖南长沙 410073
2. 拉夫堡大学计算机系, 英国莱斯特拉夫堡 LE11 3TU

收稿日期:2019-04-24 出版日期:2020-01-01 发布日期:2019-12-23
通讯作者: 吴亚辉 E-mail:mawubin417@163.com;ruiwang@nudt.edu.cn;w.wang3@lboro.ac.uk;yahui_wu@nudt.edu.cn;sudeng@nudt.edu.cn;hbhuang@nudt.edu.cn
作者简介:马武彬(1986-),男,讲师,博士,主要研究方向为多目标优化、微服务、数据挖掘。E-mail:mawubin417@163.com|王锐(1986-),男,副研究员,博士,主要研究方向为多目标优化、数据挖掘、信息处理。E-mail:ruiwang@nudt.edu.cn|王威超(1986-),男,博士研究生,主要研究方向为机器智能、深度学习。E-mail:w.wang3@lboro.ac.uk|邓苏(1963-),男,教授,博士,主要研究方向为信息综合处理与辅助决策。E-mail:sudeng@nudt.edu.cn|黄宏斌(1975-),男,教授,博士,主要研究方向为信息处理。E-mail:hbhuang@nudt.edu.cn
基金资助:
国家自然科学基金(61871388);国家自然科学基金(61773390);湖南省自然科学基金(2018JJ3619);湖湘青年英才计划(2018RS3081)

Micro-service composition deployment and scheduling strategy based on evolutionary multi-objective optimization

Wubin MA^1,²(), Rui WANG¹(), Weichao WANG²(), Yahui WU^1,*(), Su DENG¹(), Hongbin HUANG¹()

1. Science and Technology on Information System Engineering Laboratory, National University of Defense Technology, Changsha 410073, China
2. Computer Science Department, Loughborough University, Loughborough LE11 3TU, UK

Received:2019-04-24 Online:2020-01-01 Published:2019-12-23
Contact: Yahui WU E-mail:mawubin417@163.com;ruiwang@nudt.edu.cn;w.wang3@lboro.ac.uk;yahui_wu@nudt.edu.cn;sudeng@nudt.edu.cn;hbhuang@nudt.edu.cn
Supported by:
国家自然科学基金(61871388);国家自然科学基金(61773390);湖南省自然科学基金(2018JJ3619);湖湘青年英才计划(2018RS3081)

摘要/Abstract

摘要：

面向微服务实例在不同资源中心的组合部署与调度问题，构建微服务组合部署与调度最优化问题模型。以资源服务中心计算及存储资源利用率、负载均衡率和微服务实际使用率等为优化目标，以服务的完备性、资源与存储资源总量和微服务序列总量为约束条件，提出基于进化多目标优化算法（NSGA-Ⅲ，MOEA/D）求解方法，寻求微服务序列在不同资源中心的实例组合部署与调度策略。通过真实数据集实验对比，在全部满足用户服务请求的约束下，该策略比传统微服务组合调度策略的计算、存储资源平均空闲率和微服务实际空闲率要分别低13.21%、5.2%和16.67%。

关键词: 微服务, 服务组合优化, 基于参考点非支配排序遗传算法, 基于分解的多目标进化算法, 多目标优化

Abstract:

For the combined deployment and scheduling of micro-service instances in different resource centers, the micro-service combination deployment and scheduling optimization problem model is built, and the resource service center computing and storage resource utilization, load balancing rate and service actual usage rate are optimized. With the completeness of service, the total amount of resources and storage resources and the total number of micro-service sequences, the evolutionary multi-objective optimization algorithm (NSGA-Ⅲ, MOEA/D) is used to solve the example combination deployment and scheduling strategy of micro-service sequences in different resource centers. Compared with the traditional data set in the some condition, the proposed strategy calculates the storage resources, the computing usage rate, and the actual service usage are reduced by 13.21%, 5.2% and 16.67%.

Key words: micro-service, service composition optimization, non-dominated sorted genetic algorithm-Ⅲ (NSGA-Ⅲ), multi-objective evolutionary algorithm based on decomposition (MOEA/D), multi-objective optimization

中图分类号:

TP393

马武彬, 王锐, 王威超, 吴亚辉, 邓苏, 黄宏斌. 基于进化多目标优化的微服务组合部署与调度策略[J]. 系统工程与电子技术, 2020, 42(1): 90-100.

Wubin MA, Rui WANG, Weichao WANG, Yahui WU, Su DENG, Hongbin HUANG. Micro-service composition deployment and scheduling strategy based on evolutionary multi-objective optimization[J]. Systems Engineering and Electronics, 2020, 42(1): 90-100.

图/表 10

图1

表1

表2

图2

图3

图4

图5

图6

图7

图8

参考文献 32

1	KWAN A, JACOBSEN H A, CHAN A, et al. Microservices in the modern software world[C]//Proc.of the 26th Annual International Conference on Computer Science and Software Engineering, 2016: 297-299.
2	VIGGIATO M, TERRA R, ROCHA H, et al. Microservices in practice: a survey study[C]//Proc.of the 6th Brazilian Workshop on Software Visualization, Evolution, and Maintenance, 2018: 189-198.
3	HASSAN S, BAHSOON R, KAZMAN R. Microservice transition and its granularity problem: a systematic mapping study[J]. arXiv preprint, 2019: arXiv: 1903.11665.
4	ALSHUQAYRAN N, ALI N, EVANS R. Towards micro service architecture recovery: an empirical study[C]//Proc.of the IEEE International Conference on Software Architecture, 2018: 4701-4709.
5	HEORHIADI V, JAMJOOM H T, RAJAGOPALAN S. Failure recovery testing framework for microservice-based applications[P]. America, 842045, 2017.
6	VILLAMIZAR M, GARCES O, OCHOA L, et al. Infrastructure cost comparison of running web applications in the cloud using AWS lambda and monolithic and microservice architectures[C]//Proc.of the 16th IEEE/ACM International Symposium on Cluster, Cloud and Grid Computing, 2016: 179-182.
7	DAYA S, VAN DUY N, EATI K, et al. Microservices from theory to practice: creating applications in IBM bluemix using the microservices approach[EB/OL].[2018-06-05]. http://www.redbooks.ibm.com/abstracts/sg248275.html.
8	SHARMA D , ANANDAN R , MANIKANDAN A , et al. Building micro service for user engagement[J]. International Journal of Engineering&Technology, 2018, 7 (4): 420- 422.
9	PICCIALLI F , BENEDUSI P , AMATO F . S-InTime:a social cloud analytical service oriented system[J]. Future Generation Computer Systems, 2018, 80 (3): 229- 241.
10	REN Z, WANG W, WU G, et al. Migrating web applications from monolithic structure to microservices architecture[C]//Proc.of the 10th Asia-Pacific Symposium on Internetware, 2018: 3555-3559.
11	ZHU X . Case Ⅱ:micro platform, major innovation-WeChat-based ecosystem of innovation[M]. Singapore: Springer, 2018: 33- 52.
12	BOUZARY H , CHEN F F . Service optimal selection and composition in cloud manufacturing:a comprehensive survey[J]. The International Journal of Advanced Manufacturing Technology, 2018, 97 (1/4): 795- 808.
13	LAHMAR F , MEZNI H . Multicloud service composition:a survey of current approaches and issues[J]. Journal of Software:Evolution and Process, 2018, 30 (10): 1- 24.
14	ZHANG Y , TAO F , LIU Y , et al. Long short-term utility aware optimal selection of manufacturing service composition towards Industrial Internet platform[J]. IEEE Trans.on Industrial Informatics, 2019, 15 (6): 3712- 3722. doi: 10.1109/TII.2019.2892777
15	林闯, 陈莹, 黄霁崴, 等. 服务计算中服务质量的多目标优化模型与求解研究[J]. 计算机学报, 2015, 38 (10): 1907- 1923. doi: 10.11897/SP.J.1016.2015.01907
	LIN C , CHEN Y , HUANG J W , et al. A survey on models and solutions of multi-objective optimization for qos in services computing[J]. Chinese Journal of Computers, 2015, 38 (10): 1907- 1923. doi: 10.11897/SP.J.1016.2015.01907
16	SEGHIR F , KHABABA A . A hybrid approach using genetic and fruit fly optimization algorithms for QoS-aware cloud service composition[J]. Journal of Intelligent Manufacturing, 2018, 29 (8): 1773- 1792. doi: 10.1007/s10845-016-1215-0
17	HUANG J, LI S, DUAN Q, et al. QoS correlation-aware service composition for unified network-cloud service provi-sioning[C]//Proc.of the IEEE Global Communications Conference, 2016: 1-6.
18	WANG T , LI C , YUAN Y , et al. An evolutionary game approach for manufacturing service allocation management in cloud manufacturing[J]. Computers&Industrial Engineering, 2019, 133 (1): 231- 240.
19	ZHOU J , YAO X . Multi-population parallel self-adaptive differential artificial bee colony algorithm with application in large-scale service composition for cloud manufacturing[J]. Applied Soft Computing, 2017, 56 (3): 379- 397.
20	BOUZARY H , CHEN F F . A hybrid grey wolf optimizer algorithm with evolutionary operators for optimal QoS-aware service composition and optimal selection in cloud manufacturing[J]. The International Journal of Advanced Manufacturing Technology, 2019, 101 (9/12): 2771- 2784.
21	NA J, LIN K J, HUANG Z, et al. An evolutionary game approach on iot service selection for balancing device energy consumption[C]//Proc.of the 12th IEEE International Conference on e-Business Engineering, 2015: 331-338.
22	SAMANTA A, LI Y, ESPOSITO F. Battle of microservices: towards latency-optimal heuristic scheduling for edge computing[C]//Proc.of the IEEE Conference on Network Softwarization, 2019: 298-308.
23	LLOYD W, RAMESH S, CHINTHALAPATI S, et al. Serverless computing: an investigation of factors influencing microservice performance[C]//Proc.of the IEEE International Conference on Cloud Engineering, 2018: 159-169.
24	BACK T, ANDRIKOPOULOS V. Using a microbenchmark to compare function as a service solution[C]//Proc.of the European Conference on Service-Oriented and Cloud Computing, 2018: 146-160.
25	FILIP I D , POP F , SERBANESCU C , et al. Microservices scheduling model over heterogeneous cloud-edge environments as support for iot applications[J]. IEEE Internet of Things Journal, 2018, 5 (4): 2672- 2681. doi: 10.1109/JIOT.2018.2792940
26	AHUJA R P S, NEDBAL M, SREEDHAR R. Systems and methods for deploying microservices in a networked microservices system[P]. American: 15/338-001, 2018.
27	JAMSHIDI P , PAHL C , MENDON A N C , et al. Microservices:the journey so far and challenges ahead[J]. IEEE Software, 2018, 35 (3): 24- 35. doi: 10.1109/MS.2018.2141039
28	BALMAKHTAR M, PERSSON C J, RAJAGOPAL A. Secure cloud computing framework[P]. America: 10/243-959, 2019.
29	QU B , ZHU Y , JIAO Y , et al. A survey on multi-objective evolutionary algorithms for the solution of the environmental economic dispatch problems[J]. Swarm and Evolutionary Computation, 2018, 38, 1- 11. doi: 10.1016/j.swevo.2017.06.002
30	DEB K , PRATAP A , AGARWAL S , et al. A fast and elitist multi-objective genetic algorithm:NSGA-Ⅱ[J]. IEEE Trans.on Evolutionary Computation, 2002, 6 (2): 182- 197. doi: 10.1109/4235.996017
31	DEB K , JAIN H . An evolutionary many-objective optimization algorithm using reference-point-based nondominated sorting approach, part Ⅰ:solving problems with box constraints[J]. IEEE Trans.on Evolutionary Computation, 2014, 18 (4): 577- 601. doi: 10.1109/TEVC.2013.2281535
32	ZHANG Q , LI H . MOEA/D:a multiobjective evolutionary algorithm based on decomposition[J]. IEEE Trans.on Evolutionary Computation, 2007, 11 (6): 712- 731. doi: 10.1109/TEVC.2007.892759

参数名称	方法或取值
交叉算子	模拟二进制交叉
交叉概率	0.9
交叉分布索引值	20
变异方法	多项式变异方法
变异概率	1.0 /种群数量
变异分布索引值	20
最大迭代次数	100
种群数量	500(当用户数超过50时为1500)

参数名称	方法或取值
交叉算子	差分进化算子
缩放因子	0.5
交叉因子	0.5
变异方法	多项式变异方法
变异概率	1.0 /种群数量
变异分布索引值	20
邻居选择概率	0.1
最大迭代次数	100
种群数量	500(当用户数超过50时为1 500)

[1]	闫世瑛, 颜克斐, 方伟, 陆恒杨. 基于差分进化邻域自适应的大规模多目标算法[J]. 系统工程与电子技术, 2022, 44(7): 2112-2124.
[2]	刘乾, 鲁云军, 陈克斌, 韩梦瑶, 郭亮. 任务主体二元约束下作战任务分解EVA方法[J]. 系统工程与电子技术, 2022, 44(7): 2201-2210.
[3]	来磊, 邹鲲, 吴德伟, 李保中. 交互策略改进MOFA进化的多UAV协同航迹规划[J]. 系统工程与电子技术, 2021, 43(8): 2282-2289.
[4]	崔荣伟, 韩维, 苏析超, 王立国, 刘玉杰. 舰载机甲板机务勤务保障作业调度与资源配置集成优化[J]. 系统工程与电子技术, 2021, 43(7): 1884-1893.
[5]	周晶, 赵晓哲, 许震, 林众, 张晓盼. 基于D-NSGA-Ⅲ算法的无人机群高维多目标任务分配方法[J]. 系统工程与电子技术, 2021, 43(5): 1240-1247.
[6]	夏博远, 杨克巍, 杨志伟, 张小可, 赵丹玲. 基于杀伤网评估的装备组合多目标优化[J]. 系统工程与电子技术, 2021, 43(2): 399-409.
[7]	田春明, 杨安, 叶乐, 李建星, 贺雨晨. 基于贝叶斯算法的天线端到端优化[J]. 系统工程与电子技术, 2021, 43(12): 3413-3419.
[8]	来磊, 吴德伟, 邹鲲, 韩昆, 李海林. 基于多准则交互膜进化算法的UAV三维航迹规划[J]. 系统工程与电子技术, 2021, 43(1): 138-146.
[9]	刘庆国, 刘新学, 武健, 李亚雄, 陈豪. 基于改进NSGA-Ⅲ的多SGSW火力分配优化[J]. 系统工程与电子技术, 2020, 42(9): 1995-2002.
[10]	王亚东, 石全, 夏伟, 陈材. 基于超启发式算法的备件供应网络结构优化[J]. 系统工程与电子技术, 2020, 42(3): 620-629.
[11]	丁春山. 传感器管理技术研究现状与展望[J]. 系统工程与电子技术, 2020, 42(12): 2761-2770.
[12]	李瑞阳, 王智学, 禹明刚, 何红悦. 基于鲁棒能力的体系多目标组合优化[J]. 系统工程与电子技术, 2019, 41(5): 1034-1042.
[13]	孙鹏, 武君胜, 王勋, 焦志强, 张杰勇. 基于多目标优化的C2组织平台资源动态调度方法[J]. 系统工程与电子技术, 2019, 41(4): 793-800.
[14]	褚骁庚, 马政伟, 陈行军. 面向多目标优化火力目标分配问题的前瞻式边际贪婪算法[J]. 系统工程与电子技术, 2019, 41(10): 2252-2259.
[15]	徐浩, 邢清华, 王伟. 基于模糊多目标规划的防空反导火力分配[J]. 系统工程与电子技术, 2018, 40(3): 563-570.

基于进化多目标优化的微服务组合部署与调度策略

Micro-service composition deployment and scheduling strategy based on evolutionary multi-objective optimization

RichHTML

PDF (PC)

可视化

摘要/Abstract

引用本文

使用本文

图/表 10

参考文献 32

相关文章 15

编辑推荐

Metrics

本文评价