Dynamic evolution method for microservice composition systems in cloud-edge environment

doi:10.11772/j.issn.1001-9081.2022060882

Journal of Computer Applications ›› 2023, Vol. 43 ›› Issue (6): 1696-1704.DOI: 10.11772/j.issn.1001-9081.2022060882

• The 37 CCF National Conference of Computer Applications (CCF NCCA 2022) • Previous Articles Next Articles

Dynamic evolution method for microservice composition systems in cloud-edge environment

Sheng YE¹^,², Jing WANG¹^,²(), Jianfeng XIN³, Guiling WANG¹^,², Chenhong GUO¹^,²

^1.School of Information Science and Technology North China University of Technology，Beijing 100144，China
^2.Beijing Key Laboratory on Integration and Analysis of Large?Scale Stream Data （North China University of Technology），Beijing 100144，China
^3.China Cybersecurity Review Technology and Certificate Center，Beijing 100013，China

Received:2022-06-20 Revised:2022-07-28 Accepted:2022-07-29 Online:2022-08-18 Published:2023-06-10
Contact: Jing WANG
About author:YE Sheng， born in 1997， M. S. candidate. His research interests include service computing， cloud computing， business process management and collaboration.
XIN Jianfeng， born in 1985， M. S.， associate senior engineer. His research interests include information security， network security.
WANG Guiling， born in 1978， Ph. D.， research fellow. Her research interests include internet service， integration and analysis of large-scale stream data， business process management and collaboration.
GUO Chenhong， born in 1999， M. S. candidate. Her research interests include service computing， cloud computing.
Supported by:
National Key Research and Development Program of China(2018YFB1402500);International Cooperation and Exchange Program of National Natural Science Foundation of China(62061136006);Key Program of National Natural Science Foundation of China(61832004)

云边环境下微服务组合系统的动态演化方法

叶盛¹^,², 王菁¹^,²(), 辛建峰³, 王桂玲¹^,², 郭陈虹¹^,²

^1.北方工业大学信息学院, 北京 100144
^2.大规模流数据集成与分析技术北京市重点实验室(北方工业大学), 北京 100144
^3.中国网络安全审查技术与认证中心, 北京 100013

通讯作者: 王菁
作者简介:叶盛（1997—），男，浙江余姚人，硕士研究生，主要研究方向：服务计算、云计算、业务流程管理与协同
王菁（1978—），女，辽宁新宾人，副研究员，博士，CCF会员，主要研究方向：云计算、服务计算、移动计算Email：wang_jing@ncut.edu.cn
辛建峰（1985—），男，山东海阳人，副高级工程师，硕士，主要研究方向：信息安全、网络安全
王桂玲（1978—），女，山东菏泽人，研究员，博士，CCF会员，主要研究方向：互联网服务、大规模流数据集成和分析、业务流程管理与协同
郭陈虹（1999—），女，河南安阳人，硕士研究生，主要研究方向：服务计算、云计算。
基金资助:
国家重点研发计划项目(2018YFB1402500);国家自然科学基金国际（地区）合作与交流项目(62061136006);国家自然科学基金重点项目(61832004)

Abstract

Abstract:

As the uncertainty of user requirements in the cloud-edge environment causes the microservice composition logic to be dynamically adjusted with the changes of user needs， a Dynamic Evolution method for Microservice Composition system （DE4MC） in the cloud-edge environment was proposed. Firstly， the user's operation was automatically recognized to implement the corresponding algorithm strategy. Secondly， in the deployment stage， the better node was selected by the system for deployment through the deployment algorithm in the proposed method after the user submitting the business process. Finally， in the dynamic adjustment stage， the dynamic evolution was performed by the system through the dynamic adjustment algorithm in the proposed method after the user adjusting the business process instances. In both algorithms in the proposed method， the migration cost of microservice instances， the data communication cost between microservices and users， and the data flow transmission cost between microservices were comprehensively considered to select better nodes for deployment， which shortened the running time and reduced the evolution cost. In the simulation experiment， in the deployment stage， the deployment algorithm in the proposed method has average running time of all scales 9.7% lower and total evolution cost 16.8% lower than those of the combination algorithm of Heuristic Algorithm （HA） with Non-dominated Sorting Genetic Algorithm-Ⅱ （NSGA-Ⅱ）； in the dynamic adjustment stage， compared with the combination algorithm of HA and NSGA-Ⅱ， the dynamic adjustment algorithm in the proposed method has the average running time of all scales 6.3% lower， and the total evolution cost 21.7% lower. Experimental results show that the proposed method ensures timely evolution of the microservice composition system in the cloud-edge environment with low evolution cost and short business process time， and provides users with satisfactory quality of service.

Key words: cloud-edge collaboration, business process, microservice composition, microservice scheduling, microservice evolution

摘要：

针对云边环境下用户需求不确定导致微服务组合逻辑会随着用户需求的变化而动态调整的问题，提出了云边环境下的微服务组合系统的动态演化方法（DE4MC）。首先，自动识别用户的操作并进行相应的算法策略；其次，在部署阶段，用户提交业务流程之后，系统通过所提方法中的部署算法选择较优的节点进行部署；最后，在动态调整阶段，用户调整业务流程实例后，系统通过所提方法中的动态调整算法进行动态演化。所提方法中的两个算法均综合考虑微服务实例的迁移代价、微服务与用户的数据通信代价和微服务之间的数据流传输代价以选择较优的节点进行部署，从而缩短了运行时间，降低了演化开销。在仿真实验中，在部署阶段，所提方法的部署算法与启发式算法（HA）+二代非支配排序遗传算法（NSGA-Ⅱ）的算法组合相比，各个规模的平均运行时间缩短了9.7%，演化总开销降低了16.8%；在动态调整阶段，所提方法的动态调整算法与HA+NSGA-Ⅱ的算法组合相比，各个规模平均运行时间缩短了6.3%，演化总开销降低了21.7%。实验结果表明，所提方法保证了云边环境下微服务组合系统能在演化开销低和业务流程时间短的条件下及时演化，并且能够提供用户满意的服务质量。

关键词: 云边协同, 业务流程, 微服务组合, 微服务调度, 微服务演化

CLC Number:

TP311.5

Sheng YE, Jing WANG, Jianfeng XIN, Guiling WANG, Chenhong GUO. Dynamic evolution method for microservice composition systems in cloud-edge environment[J]. Journal of Computer Applications, 2023, 43(6): 1696-1704.

叶盛, 王菁, 辛建峰, 王桂玲, 郭陈虹. 云边环境下微服务组合系统的动态演化方法[J]. 《计算机应用》唯一官方网站, 2023, 43(6): 1696-1704.

Figures/Tables 9

Fig. 1 Evolution architecture of microservice system

Fig. 2 Dynamic evolution method of microservice composition system

Tab. 1 Evolution operations in deployment and adjustment stages

演化操作顺序	部署阶段演化操作	调整阶段演化操作
1	$D e p l o y (b 1, τ i (“ 受理订单 ”))$	$R e m o v e (τ (“ 飞机运输 ”))$
2	$A d j u s t (τ (“ 受理订单 ”), L i, L j)$	$R e m o v e (τ (“ 货车运输 ”))$
3	$A d d (τ (C o m p o s i t e (I (s (“ 上门揽件 ”)), I (s (“ 支付服务 ”)), I (s (“ 货车运输 ”))), “ 广州天河区猎德村附近边缘节点 ”, L 5)$	$S w i t c h (b 3, τ i (“ 高铁运输 ”), τ j (“ 高铁运输 ”))$
4	$D e p l o y (b 2, τ i (“ 组合服务 ”))$	$A d d (τ (“ 火车运输 ”), “ 天津武清区集散中心附近边缘节点 ”, L 4)$
5	$D e p l o y (b 3, τ i (“ 飞机运输 ”))$	$D e p l o y (b 4, τ i (“ 火车运输 ”))$
6	$A d d ((τ (“ 货车运输 ”), “ 北京通州区集散中心附近边缘节点 ”,$ $L 3)$	$S w i t c h (b 5, τ i (“ 货车运输 ”), τ j (“ 货车运输 ”))$
7	$D e p l o y (b 4, τ i (“ 货车运输 ”))$	$A d j u s t (τ (“ 货车运输 ”), L i, L j)$
8	$S w i t c h (b 5, τ i (“ 派件 ”), τ j (“ 派件 ”))$
9	$A d j u s t (τ (“ 派件 ”), L i, L j)$

Tab. 1 Evolution operations in deployment and adjustment stages

演化操作顺序	部署阶段演化操作	调整阶段演化操作
1	$D e p l o y (b 1, τ i (“ 受理订单 ”))$	$R e m o v e (τ (“ 飞机运输 ”))$
2	$A d j u s t (τ (“ 受理订单 ”), L i, L j)$	$R e m o v e (τ (“ 货车运输 ”))$
3	$A d d (τ (C o m p o s i t e (I (s (“ 上门揽件 ”)), I (s (“ 支付服务 ”)), I (s (“ 货车运输 ”))), “ 广州天河区猎德村附近边缘节点 ”, L 5)$	$S w i t c h (b 3, τ i (“ 高铁运输 ”), τ j (“ 高铁运输 ”))$
4	$D e p l o y (b 2, τ i (“ 组合服务 ”))$	$A d d (τ (“ 火车运输 ”), “ 天津武清区集散中心附近边缘节点 ”, L 4)$
5	$D e p l o y (b 3, τ i (“ 飞机运输 ”))$	$D e p l o y (b 4, τ i (“ 火车运输 ”))$
6	$A d d ((τ (“ 货车运输 ”), “ 北京通州区集散中心附近边缘节点 ”,$ $L 3)$	$S w i t c h (b 5, τ i (“ 货车运输 ”), τ j (“ 货车运输 ”))$
7	$D e p l o y (b 4, τ i (“ 货车运输 ”))$	$A d j u s t (τ (“ 货车运输 ”), L i, L j)$
8	$S w i t c h (b 5, τ i (“ 派件 ”), τ j (“ 派件 ”))$
9	$A d j u s t (τ (“ 派件 ”), L i, L j)$

Fig. 3 Algorithmic strategy

Tab. 2 Workflow parameters setting

参数	设定范围
任务数	［10，50］
最大并行度	［1，4］
最大路径长度	［5，20］
不同活动之间数据传输量/MB	［0，0.5］
每个活动的用户位置	［1，11］

Tab. 3 Evolution operation cost

演化操作	演化开销	演化操作	演化开销
Add	6.50	Swith	0.08
Remove	1.20	Composite	50.00
Adjust	7.70	Deploy	0.04
Split	0.00	Keep	0.00
Retain	0.00

Tab. 4 Genetic algorithm parameter setting

参数	值	参数	值
种群规模	100	交叉概率	0.50
进化代数	200	变异概率	0.01

Fig. 4 Running time of each algorithm in different stages

Fig. 5 Total evolution cost of each algorithm in different stages

References 24

1	COCCONI D， VILLARREAL P. Microservices-based approach for a collaborative business process management cloud platform［C］// Proceedings of the XLVI Latin American Computing Conference. Piscataway： IEEE， 2020： 128-137. 10.1109/clei52000.2020.00022
2	OBERHAUSER R. Microflows： lightweight automated planning and enactment of workflows comprising semantically-annotated microservices［C］// Proceedings of the 2016 International Symposium on Business Modeling and Software Design， LNBIP 275. Cham： Springer， 2017： 183-199. 10.5220/0006223001340143
3	HADJ YAHIA E BEN， RÉVEILLÉRE L， BROMBERG Y D， et al. Medley： an event-driven lightweight platform for service composition［C］// Proceedings of the 2016 International Conference on Web Engineering， LNCS 9671. Cham： Springer， 2016： 3-20.
4	MONTEIRO D， GADELHA R， MAIA P H M， et al. Beethoven： an event-driven lightweight platform for microservice orchestration［C］// Proceedings of the 2018 European Conference on Software Architecture， LNCS 11048. Cham： Springer， 2018： 191-199.
5	GAO M， CHEN M X， LIU A， et al. Optimization of microservice composition based on artificial immune algorithm considering fuzziness and user preference［J］. IEEE Access， 2020， 8： 26385-26404. 10.1109/access.2020.2971379
6	VALDERAS P， TORRES V， PELECHANO V. A microservice composition approach based on the choreography of BPMN fragments［J］. Information and Software Technology， 2020， 127： No.106370. 10.1016/j.infsof.2020.106370
7	SAFINA L， MAZZARA M， MONTESI F， et al. Data-driven workflows for microservices： genericity in Jolie［C］// Proceedings of the IEEE 30th International Conference on Advanced Information Networking and Applications. Piscataway： IEEE， 2016： 430-437. 10.1109/aina.2016.95
8	DECKER G， KOPP O， LEYMANN F， et al. Modeling service choreographies using BPMN and BPEL4Chor ［C］// Proceedings of the 2008 International Conference on Advanced Information Systems Engineering， LNCS 5074. Berlin： Springer， 2008： 79-93.
9	GUTIÉRREZ-FERNÁNDEZ A M， RESINAS M， RUIZ-CORTÉS A. Redefining a process engine as a microservice platform［C］// Proceedings of the 2016 International Conference on Business Process Management， LNBIP 281. Cham： Springer， 2017： 252-263. 10.1007/978-3-319-58457-7_19
10	ORTIZ J， TORRES V， VALDERAS P. Characterization of bottom-up microservice composition evolution： an approach based on the choreography of BPMN fragments ［C］// Proceedings of the ER Forum， Demo and Posters 2020 Co-located with 39th International Conference on Conceptual Modeling. Aachen： CEUR-WS.org， 2020： 101-114. 10.1016/j.infsof.2020.106370
11	JAYAWARDANA Y， FERNANDO R， JAYAWARDENA G， et al. A full stack microservices framework with business modelling ［C］// Proceedings of the 18th International Conference on Advances in ICT for Emerging Regions. Piscataway： IEEE， 2018： 78-85. 10.1109/icter.2018.8615473
12	VALDERAS P， TORRES V， SERRAL E. Modelling and executing IoT-enhanced business processes through BPMN and microservices［J］. Journal of Systems and Software， 2022， 184： No.111139. 10.1016/j.jss.2021.111139
13	DAI F， MO Q， QIANG Z P， et al. A choreography analysis approach for microservice composition in cyber-physical-social systems ［J］. IEEE Access， 2020， 8： 53215-53222. 10.1109/access.2020.2980891
14	GIALLORENZO S， LANESE I. Choreographies for microservices［EB/OL］［2022-05-13］..
15	辛园园，李俊晖，李阵. 微服务组合方法研究进展［J］. 无线通信技术， 2018， 27（3）：42-46. 10.3969/j.issn.1003-8329.2018.03.009
	XIN Y Y， LI J H， LI Z. Research progress of microservices composition methods［J］. Wireless Communication Technology， 2018， 27（3）：42-46. 10.3969/j.issn.1003-8329.2018.03.009
16	MONTESI F， GUIDI C， ZAVATTARO G. Service-oriented programming with Jolie ［M］// BOUGUETTAYA A， SHENG Q Z， DANIEL F. Web Services Foundations. New York： Springer， 2014： 81-107. 10.1007/978-1-4614-7518-7_4
17	贺祥，刘磊，涂志莹，等. 多版本共存的微服务系统自适应演化方法［J］. 软件学报， 2021， 32（5）：1341-1359.
	HE X， LIU L， TU Z Y， et al. Self-adaptative evoluationary method of multi-version coexisting microservice systems［J］. Journal of Software， 2021， 32（5）： 1341-1359.
18	HE X， TU Z Y， XU X F， et al. Programming framework and infrastructure for self-adaptation and optimized evolution method for microservice systems in cloud-edge environments ［J］. Future Generation Computer Systems， 2021， 118： 263-281. 10.1016/j.future.2021.01.008
19	WANG T， HE X， XU H C， et al. EPF4M： an evolution-oriented programming framework for microservices ［C］// Proceedings of the 2021 IEEE International Conference on Services Computing. Piscataway： IEEE， 2021： 174-182. 10.1109/scc53864.2021.00030
20	KEPHART J O， CHESS D M. The vision of autonomic computing ［J］. Computer， 2003， 36（1）： 41-50. 10.1109/mc.2003.1160055
21	GAO M， CHEN M X， LIU A， et al. Optimization of microservice composition based on artificial immune algorithm considering fuzziness and user preference ［J］. IEEE Access， 2020， 8： 26385-26404. 10.1109/access.2020.2971379
22	LIN M， XI J Q， BAI W H， et al. Ant colony algorithm for multi-objective optimization of container-based microservice scheduling in cloud ［J］. IEEE Access， 2019， 7： 83088-83100. 10.1109/access.2019.2924414
23	DEB K， AGRAWAL S， PRATAP A， et al. A fast elitist non-dominated sorting genetic algorithm for multi-objective optimization： NSGA-Ⅱ［C］// Proceedings of the 2000 International Conference on Parallel Problem Solving from Nature， LNCS 1917. Berlin： Springer， 2000： 849-858.
24	CANON L C， SAYAH M EL， HÉAM P C. A comparison of random task graph generation methods for scheduling problems［C］// Proceedings of the 2019 European Conference on Parallel Processing， LNTCS 11725. Cham： Springer， 2019： 61-73.

[1]	Shangjing LIN, Ji MA, Bei ZHUANG, Yueying LI, Ziyi LI, Tie LI, Jin TIAN. Wireless traffic prediction based on federated learning [J]. Journal of Computer Applications, 2023, 43(6): 1900-1909.
[2]	WANG Yijie, FAN Jiafei, WANG Chenyu. Two-stage task offloading strategy based on game theory in cloud-edge environment [J]. Journal of Computer Applications, 2021, 41(5): 1392-1398.
[3]	MAO Xinyi, NIU Jun, DING Xueer, ZHANG Kaile. QoS verification of microservice composition platform based on model checking [J]. Journal of Computer Applications, 2020, 40(11): 3267-3272.
[4]	qian deFu. Study of VO process based on migrating workflow system in VBE [J]. Journal of Computer Applications, 2010, 30(2): 394-397.
[5]	. Study of data race for process based on BPEL [J]. Journal of Computer Applications, 2010, 30(07): 1959-1961.
[6]	. Modeling and simulation optimization of purchasing process based on Petri net [J]. Journal of Computer Applications, 2009, 29(10): 2871-2874.
[7]	. Web service selection based on genetic algorithm with tree dual-structure code [J]. Journal of Computer Applications, 2008, 28(12): 3017-3020.
[8]	Guo-feng ZHANG Jun HE Cong-fu XU. BPEL based method for general security control module design [J]. Journal of Computer Applications, 2008, 28(11): 2926-2928.
[9]	. Process model for e-government business processing [J]. Journal of Computer Applications, 2007, 27(8): 2072-2076.
[10]	. Design of workflow model interoperation based on rule [J]. Journal of Computer Applications, 2007, 27(2): 400-402.
[11]	. Discussion of BPEL4WS-based Web service integration [J]. Journal of Computer Applications, 2007, 27(11): 2733-2735.
[12]	. Dynamic composition of Web service workflow based on semantic description [J]. Journal of Computer Applications, 2007, 27(11): 2725-2727.
[13]	峰刘 . Coloured Petri net extended with price information and its application [J]. Journal of Computer Applications, 2007, 27(10): 2501-2503.
[14]	. Study on the retrieve and re-use method of business process performance model repository [J]. Journal of Computer Applications, 2007, 27(1): 180-182.
[15]	. Research on integrated service level agreements system architecture and application [J]. Journal of Computer Applications, 2006, 26(6): 1260-1262.

Dynamic evolution method for microservice composition systems in cloud-edge environment

云边环境下微服务组合系统的动态演化方法

RichHTML

PDF

Knowledge

Abstract

Cite this article

share this article

Figures/Tables 9

References 24

Related Articles 15

Recommended Articles

Metrics