Service Decoupling and Deployment Strategy for Edge Computing

Li Liying; Zhang Runze; Wei Tongquan

doi:10.7544/issn1000-1239.202220736

Journal of Computer Research and Development > 2023 > 60(5): 1073-1085. > DOI: 10.7544/issn1000-1239.202220736

Li Liying, Zhang Runze, Wei Tongquan. Service Decoupling and Deployment Strategy for Edge Computing[J]. Journal of Computer Research and Development, 2023, 60(5): 1073-1085. DOI: 10.7544/issn1000-1239.202220736

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Service Decoupling and Deployment Strategy for Edge Computing

1.
School of Computer Science and Technology, East China Normal University, Shanghai 200062
2.
School of Computer Science and Engineering, Nanjing University of Science and Technology, Nanjing 210094

Funds: This work was supported by the General Program of the National Natural Science Foundation of China (62272169), the Shanghai Municipal Science and Technology Major Project (2021SHZDZX), and the Project of Shanghai Trusted Industry Internet Software Collaborative Innovation Center.

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Author Bio:
Li Liying: born in 1995. PhD, lecturer. Her main research interests include Internet of things, mobile edge computing, and data analysis

Zhang Runze: born in 1997. master. His main research interests include mobile edge computing and cloud computing

Wei Tongquan: born in 1973. PhD, associate professor. His main research interests include Internet of things, mobile edge computing, and cloud computing
Received Date: August 21, 2022
Revised Date: March 29, 2023
Available Online: April 09, 2023

Graphical Abstract

Abstract

Abstract

In the era of the Internet of everything, the huge transmission delays between massive devices and data, clouds and devices have brought multiple challenges to developing applications, processing data, and ultimately improving the quality of service(QoS). As a result, edge computing architectures that significantly reduce the amount of data transferred to cloud servers and the response time of service requests by deploying computing power near end devices have emerged. However, load balancing, security, and mobility of edge devices are still the key points that affect the quality of service of edge computing architectures. In order to solve the above problems, we propose a two-stage QoS optimization scheme to solve the service deployment problem under the mobile edge computing architecture. In the first stage, we consider the decoupling of services and the load balancing of edge servers, model the problem of service deployment, propose a real-time decoupling scheme for centralized services, and design a static deployment strategy; In the second stage, we consider the mobility of edge devices, design a dynamic deployment strategy for device mobility-aware services, and optimize the quality of service under the mobile edge computing architecture. The experimental results on two datasets show that the static deployment strategy proposed in this paper can reduce the response time of service requests by 36%, and the dynamic deployment strategy can further reduce the service response time by 13%.
- edge computing,
- quality of service(QoS),
- response time,
- service decoupling,
- service deployment

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References (31)

References

[1]	施巍松,孙辉,曹杰,等. 边缘计算:万物互联时代新型计算模型[J]. 计算机研究与发展,2017,54(5):907−924 doi: 10.7544/issn1000-1239.2017.20160941 Shi Weisong, Sun Hui, Cao Jie, et al. Edge computing—An emerging computing model for the Internet of everything era[J]. Journal of Computer Research and Development, 2017, 54(5): 907−924 (in Chinese) doi: 10.7544/issn1000-1239.2017.20160941
[2]	Cleber S, Leandro A, Flávia D, et al. Increasing the availability of IoT applications with reactive microservices[J]. Service Oriented Computing and Applications, 2021, 15(2): 109−126 doi: 10.1007/s11761-020-00308-8
[3]	钱志鸿,王义君. 物联网技术与应用研究[J]. 电子学报,2012,40(5):1023−1029 doi: 10.3969/j.issn.0372-2112.2012.05.026 Qian Zhihong, Wang Yijun. IoT technology and application[J]. Acta Electronica Sinica, 2012, 40(5): 1023−1029 (in Chinese) doi: 10.3969/j.issn.0372-2112.2012.05.026
[4]	Xiao Yang, Li Haizhong, Li Bo. Bandwidth sharing schemes for multimedia traffic in the IEEE 802.11e contention-based WLANs[J]. IEEE Transactions on Mobile Computing, 2007, 6(7): 815−831 doi: 10.1109/TMC.2007.1054
[5]	Auer F, Lenarduzzi V, Felderer M, et al. From monolithic systems to Microservices: An assessment framework[J]. Information and Software Technology, 2021, 137: 106600
[6]	Arisholm E, Briand L C, Foyen A. Dynamic coupling measurement for object-oriented software[J]. IEEE Transactions on Software Engineering, 2004, 30(8): 491−506 doi: 10.1109/TSE.2004.41
[7]	Poshyvanyk D, Marcus A, Ferenc R, et al. Using information retrieval based coupling measures for impact analysis[J]. Empirical Software Engineering, 2009, 14(1): 5−32 doi: 10.1007/s10664-008-9088-2
[8]	Wang Xiaofei, Han Yiwen, Leung V C M, et al. Convergence of edge computing and deep learning: A comprehensive survey[J]. IEEE Communications Surveys & Tutorials, 2020, 22(2): 869−904
[9]	Shi Weisong, Cao Jie, Zhang Quan, et al. Edge computing: Vision and challenges[J]. IEEE Internet of Things Journal, 2016, 3(5): 637−646 doi: 10.1109/JIOT.2016.2579198
[10]	Yin Hao, Zhang Xu, Liu Hongqiang Harry, et al. Edge provisioning with flexible server placement[J]. IEEE Transactions on Parallel and Distributed Systems, 2016, 28(4): 1031−1045
[11]	Farhadi V, Mehmeti F, He Ting, et al. Service placement and request scheduling for data-intensive applications in edge clouds[J]. IEEE/ACM Transactions on Networking, 2021, 29(2): 779−792 doi: 10.1109/TNET.2020.3048613
[12]	Dong Yunmeng, Xu Gaochao, Ding Yan, et al. A ‘joint-me’ task deployment strategy for load balancing in edge computing[J]. IEEE Access, 2019, 7: 99658−99669 doi: 10.1109/ACCESS.2019.2928582
[13]	樊琦,李卓,陈昕. 基于边缘计算的分支神经网络模型推断延迟优化[J]. 计算机应用,2020,40(2):342−346 doi: 10.11772/j.issn.1001-9081.2019081406 Fan Qi, Li Zhuo, Chen Xin. Inference delay optimization of branchy neural network model based on edge computing[J]. Journal of Computer Applications, 2020, 40(2): 342−346 (in Chinese) doi: 10.11772/j.issn.1001-9081.2019081406
[14]	Ahmed E, Rehmani M H. Mobile edge computing: Opportunities, solutions, and challenges[J]. Future Generation Computer Systems, 2017, 70: 59−63 doi: 10.1016/j.future.2016.09.015
[15]	Chen Xu, Shi Qian, Yang Lei, et al. ThriftyEdge: Resource-efficient edge computing for intelligent IoT applications[J]. IEEE Network, 2018, 32(1): 61−65 doi: 10.1109/MNET.2018.1700145
[16]	Deng Tao, You Lei, Fan Pingzhi, et al. Device caching for network offloading: Delay minimization with presence of user mobility[J]. IEEE Wireless Communications Letters, 2018, 7(4): 558−561 doi: 10.1109/LWC.2018.2795617
[17]	Wang Shiqiang, Urgaonkar R, Zafer M, et al. Dynamic service migration in mobile edge-clouds[C] //Proc of IFIP Networking Conf. Piscataway, NJ: IEEE, 2015: 1−9
[18]	Taleb T, Ksentini A. An analytical model for follow me cloud[C]// Proc of the 2013 IEEE Global Communications Conf (GLOBECOM). Piscataway, NJ: IEEE, 2013: 1291−1296
[19]	Cao Kun, Li Liying, Cui Yangguang, et al. Exploring placement of heterogeneous edge servers for response time minimization in mobile edge-cloud computing[J]. IEEE Transactions on Industrial Informatics, 2020, 17(1): 494−503
[20]	Jiang Wei, Jiang Ke, Zhang Xia, et al. Energy aware real-time scheduling policy with guaranteed security protection[C]// Proc of the 19th Asia and South Pacific Design Automation Conf (ASP-DAC). Piscataway, NJ: IEEE, 2014: 317−322
[21]	Wang Shangguang, Guo Yan, Zhang Ning, et al. Delay-aware microservice coordination in mobile edge computing: A reinforcement learning approach[J]. IEEE Transactions on Mobile Computing, 2019, 20(3): 939−951
[22]	Wei Xiaohui, Li Zijian, Liu Yuanyuan, et al. SDLSC-TA: Subarea division learning based task allocation in sparse mobile crowdsensing[J]. IEEE Transactions on Emerging Topics in Computing, 2020, 9(3): 1344−1358
[23]	Ouyang Tao, Zhou Zhi, Chen Xu. Follow me at the edge: Mobility-aware dynamic service placement for mobile edge computing[J]. IEEE Journal on Selected Areas in Communications, 2018, 36(10): 2333−2345 doi: 10.1109/JSAC.2018.2869954
[24]	Chaufournier L, Sharma P, Le F, et al. Fast transparent virtual machine migration in distributed edge clouds[C/OL]//Proc of the 2nd ACM/IEEE Symp on Edge Computing. Piscataway, NJ: IEEE, 2017 [2023-03-21].https://dl.acm.org/doi/10.1145/3132211.3134445
[25]	Ma Lele, Yi Shanhe, Li Qun. Efficient service handoff across edge servers via docker container migration[C/OL]//Proc of the 2nd ACM/IEEE Symp on Edge Computing. Piscataway, NJ: IEEE, 2017 [2023-03-23].https://dl.acm.org/doi/10.1145/3132211.3134460
[26]	Pierdomenico P. Converse Lyapunov theorems for discrete-time switching systems with given switches digraphs[J]. IEEE Transactions on Automatic Control, 2019, 64(6): 2502−2508 doi: 10.1109/TAC.2018.2867166
[27]	Chen Minghua, Liew C S, Shao Ziyu, et al. Markov approximation for combinatorial network optimization[J]. IEEE Transactions on Information Theory, 2013, 59(10): 6301−6327 doi: 10.1109/TIT.2013.2268923
[28]	Meek C, Thiesson B, Heckerman D. The learning curve method applied to clustering[C] // Proc of the 8th Int Workshop on Artificial Intelligence and Statistics. New York: PMLR, 2001: 196−202
[29]	Zhao Chenhong, Zhang Shanhan, Liu Qingfeng, et al. Independent tasks scheduling based on genetic algorithm in cloud computing[C]//Proc of the 5th Int Conf on Wireless Communications, Networking and Mobile Computing. Piscataway, NJ: IEEE, 2009: 1−4
[30]	Kennedy J, Eberhart R. Particle swarm optimization[C]//Proc of the Int Conf on Neural Networks. Piscataway, NJ: IEEE, 1995: 1942−1948
[31]	Radenkovic M, Grundy A. Efficient and adaptive congestion control for heterogeneous delay-tolerant networks[J]. Ad Hoc Networks, 2012, 10(7): 1322−1345 doi: 10.1016/j.adhoc.2012.03.013

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