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

Abstract

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

CLC Number:

TP393

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.

Figures/Tables 10

Fig.1

Table 1

Table 2

Fig.2

Fig.3

Fig.4

Fig.5

Fig.6

Fig.7

Fig.8

References 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]	Shiying YAN, Kefei YAN, Wei FANG, Hengyang LU. Large-scale multi-objective algorithm based on neighborhood adaptive of differential evolution [J]. Systems Engineering and Electronics, 2022, 44(7): 2112-2124.
[2]	Qian LIU, Yunjun LU, Kebin CHEN, Mengyao HAN, Liang GUO. Combat task decomposition EVA method based on binary constraints of task subject [J]. Systems Engineering and Electronics, 2022, 44(7): 2201-2210.
[3]	Rongwei CUI, Wei HAN, Xichao SU, Liguo WANG, Yujie LIU. Integrated optimization of carrier-based aircraft flight deck operations scheduling and resource configuration for pre-flight preparation stage [J]. Systems Engineering and Electronics, 2021, 43(7): 1884-1893.
[4]	Boyuan XIA, Kewei YANG, Zhiwei YANG, Xiaoke ZHANG, Danling ZHAO. Multi-objective optimization of equipment portfolio based on kill-web evaluation [J]. Systems Engineering and Electronics, 2021, 43(2): 399-409.
[5]	Chunming TIAN, An YANG, Le YE, Jianxing LI, Yuchen HE. End-to-end antenna optimization based on Bayesian optimization algorithm [J]. Systems Engineering and Electronics, 2021, 43(12): 3413-3419.
[6]	Lei LAI, Dewei WU, Kun ZOU, Kun HAN, Hailin LI. Three dimensional route planning of UAV based on the multi-criterion interactive membrane evolutionary algorithm [J]. Systems Engineering and Electronics, 2021, 43(1): 138-146.
[7]	Yadong WANG, Quan SHI, Wei XIA, Cai CHEN. Structure optimization of spare parts supply network based on hyper heuristic algorithm [J]. Systems Engineering and Electronics, 2020, 42(3): 620-629.
[8]	Chunshan DING. Survey on progress and prospect of sensor management [J]. Systems Engineering and Electronics, 2020, 42(12): 2761-2770.
[9]	LI Ruiyang, WANG Zhixue, YU Minggang, HE Hongyue. Multi-objective portfolio optimization of system-of-systems based on robust capabilities#br# [J]. Systems Engineering and Electronics, 2019, 41(5): 1034-1042.
[10]	SUN Peng, WU Junsheng, WANG Xun, JIAO Zhiqiang, ZHANG Jieyong. Dynamic resources scheduling method based on multi-objective optimization in C2 organization [J]. Systems Engineering and Electronics, 2019, 41(4): 793-800.
[11]	CHU Xiaogeng, MA Zhengwei, CHEN Xingjun. Look-ahead margin-greedy constructive algorithm for the multi-objective optimization of the weapon target assignment problem [J]. Systems Engineering and Electronics, 2019, 41(10): 2252-2259.
[12]	XU Hao, XING Qinghua, WANG Wei. WTA for air and missile defense based on fuzzy multi-objective programming [J]. Systems Engineering and Electronics, 2018, 40(3): 563-570.
[13]	LI Zhiliang, LI Xiaojiang, ZHANG Donglai. Proactive scheduling of agile imaging satellite based on improved differential evolution algorithm [J]. Systems Engineering and Electronics, 2018, 40(2): 353-359.
[14]	FANG Wei, ZHANG Lingzhi. Learning of fuzzy cognitive maps using multi-objective evolutionary algorithm [J]. Systems Engineering and Electronics, 2018, 40(2): 447-455.
[15]	HAN Bowen, YAO Peiyang. Coalition formation of manned/unmanned aerial vehicle cluster based on Holon organization [J]. Systems Engineering and Electronics, 2018, 40(1): 91-97.

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

RichHTML

PDF (PC)

Knowledge

Abstract

Cite this article

share this article

Figures/Tables 10

References 32

Related Articles 15

Recommended Articles

Metrics

Comments