Service Migration Algorithm Based on Markov Decision Process with Multiple Service Types and Multiple System Factors

MA Anhua; PAN Su; ZHOU Weiwei

doi:10.23919/cje.2022.00.128

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Anhua MA, Su PAN, and Weiwei ZHOU, “Service Migration Algorithm Based on Markov Decision Process with Multiple Service Types and Multiple System Factors,” Chinese Journal of Electronics, vol. x, no. x, pp. 1–11, xxxx doi: 10.23919/cje.2022.00.128

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Anhua MA, Su PAN, and Weiwei ZHOU, “Service Migration Algorithm Based on Markov Decision Process with Multiple Service Types and Multiple System Factors,” Chinese Journal of Electronics, vol. x, no. x, pp. 1–11, xxxx doi: 10.23919/cje.2022.00.128

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Service Migration Algorithm Based on Markov Decision Process with Multiple Service Types and Multiple System Factors

doi: 10.23919/cje.2022.00.128

MA Anhua^{1
,
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PAN Su^1
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ZHOU Weiwei^2
,

1.
Nanjing University of Posts and Telecommunications, Nanjing, China
2.
School of Computer Science and Technology, Nanjing Tech University, Nanjing, China

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Author Bio:
Anhua MA was born in Jiangsu, China, in 1985. Master degree, Engineer, student of Broadband Wireless Communication College, Nanjing University of Posts and Telecommunications, the main research area is broadband wireless resource management. (Email: manshmily@qq.com)

Su PAN was born in Jiangsu, China, in 1969. Doctor of Philosophy, Professor, Dean of the College of Internet of Things, Nanjing University of Posts and Telecommunications, the main research area is Broadband Wireless Resource Allocation and Mobile Internet Technology. (Email: supan@njupt.edu.cn)

Weiwei ZHOU was born in Jiangsu, China, in 1992. She received her dual Ph.D. degree from Nanjing University of Posts and Telecommunications and University of Technology Sydney in 2022. She is now working in School of Computer Science and Technology, Nanjing Tech University. Her current interests include admission control and resource allocation in the wireless networks. (Email: zhouweiwei92@163.com)
Corresponding author: Email: manshmily@qq.com
Received Date: 2022-03-22
Accepted Date: 2022-11-07

Available Online: 2024-03-20

Abstract

Abstract

This paper proposes a Markov decision process based service migration algorithm to satisfy Quality of Service(QoS) requirements when the terminals leave the original server. Services were divided into real-time services and non-real-time services, each type of them has different requirements on transmission bandwidth and latency, which were considered in the revenue function. Different values were assigned to the weight coefficients of QoS parameters for different service types in the revenue and cost functions so as to distinguish the differences between the two service types. The overall revenue was used for migration decisions, rather than fixed threshold or instant revenue. The Markov decision process was used to maximize the overall revenue of the system. Simulation results show that the proposed algorithm obtained more revenue compared with the existing works.
- Service migration,
- Markov decision process,
- Heterogeneous QoS,
- Multiple system factors

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References(20)

References

[1]	M. Chen, W. Li, G. Fortino, et al., “A dynamic service migration mechanism in edge cognitive computing,” ACM Transactions on Internet Technology, vol. 19, no. 2, article no. 30, 2019. doi: 10.1145/3239565
[2]	F. Brandherm, L. Wang, and M. Mühlhäuser, “A learning-based framework for optimizing service migration in mobile edge clouds,” in Proceedings of the 2nd International Workshop on Edge Systems, Analytics and Networking, Dresden, Germany, pp. 12–17, 2019.
[3]	C. L. Li, L. Zhu, W. G. Li, et al., “Joint edge caching and dynamic service migration in SDN based mobile edge computing,” Journal of Network and Computer Applications, vol. 177, article no. 102966, 2021. doi: 10.1016/j.jnca.2020.102966
[4]	J. K. Ren, G. D. Yu, Y. H. He, et al., “Collaborative cloud and edge computing for latency minimization,” IEEE Transactions on Vehicular Technology, vol. 68, no. 5, pp. 5031–5044, 2019. doi: 10.1109/TVT.2019.2904244
[5]	M. R. Anwar, S. G. Wang, M. F. Akram, et al., “5g-enabled MEC: A distributed traffic steering for seamless service migration of internet of vehicles,” IEEE Internet of Things Journal, vol. 9, no. 1, pp. 648–661, 2022. doi: 10.1109/JIOT.2021.3084912
[6]	R. A. Addad, D. L. C. Dutra, M. Bagaa, et al., “Fast service migration in 5G trends and scenarios,” IEEE Network, vol. 34, no. 2, pp. 92–98, 2020. doi: 10.1109/MNET.001.1800289
[7]	S. Q. Wang, R. Urgaonkar, M. Zafer, et al., “Dynamic service migration in mobile edge computing based on Markov decision process,” IEEE/ACM Transactions on Networking, vol. 27, no. 3, pp. 1272–1288, 2019. doi: 10.1109/TNET.2019.2916577
[8]	Z. P. Gao, Q. D. Jiao, K. L. Xiao, et al., “Deep reinforcement learning based service migration strategy for edge computing,” in Proceedings of the 2019 IEEE International Conference on Service-Oriented System Engineering (SOSE), San Francisco, CA, USA, pp. 116–1165, 2019.
[9]	X. B. Zhou, S. X. Ge, T. Qiu, et al., “Energy-efficient service migration for multi-user heterogeneous dense cellular networks,” IEEE Transactions on Mobile Computing, vol. 22, no. 2, pp. 890–905, 2023. doi: 10.1109/TMC.2021.3087198
[10]	H. W. Lin, X. L. Xu, J. Zhao, et al., “Dynamic service migration in ultra-dense multi-access edge computing network for high-mobility scenarios,” EURASIP Journal on Wireless Communications and Networking, vol. 2020, no. 1, article no. 191, 2020. doi: 10.1186/s13638-020-01805-2
[11]	H. Guo, L. L. Rui, and Z. P. Gao, “Dynamic service migration strategy based on MDP model with multiple parameter in vehicular edge network,” Journal on Communications, vol. 41, no. 1, pp. 1–14, 2020. doi: 10.11959/j.issn.1000-436x.2020012
[12]	M. X. Xu and R. Buyya, “Energy efficient scheduling of application components via brownout and approximate Markov decision process,” in Proceedings of the 15th International Conference on Service-Oriented Computing, Malaga, Spain, pp. 206–220, 2017.
[13]	Q. Yuan, J. L. Li, H. B. Zhou, et al., “A joint service migration and mobility optimization approach for vehicular edge computing,” IEEE Transactions on Vehicular Technology, vol. 69, no. 8, pp. 9041–9052, 2020. doi: 10.1109/TVT.2020.2999617
[14]	J. Li, X. M. Shen, L. Chen, et al., “Service migration in fog computing enabled cellular networks to support real-time vehicular communications,” IEEE Access, vol. 7, pp. 13704–13714, 2019. doi: 10.1109/ACCESS.2019.2893571
[15]	Y. Peng, L. Liu, Y. Q. Zhou, et al., “Deep reinforcement learning-based dynamic service migration in vehicular networks,” in Proceedings of the 2019 IEEE Global Communications Conference (GLOBECOM), Waikoloa, HI, USA, pp. 1–6, 2019.
[16]	A. Abouaomar, Z. Mlika, A. Filali, et al., “A deep reinforcement learning approach for service migration in MEC-enabled vehicular networks,” in Proceedings of the 2021 IEEE 46th Conference on Local Computer Networks (LCN), Edmonton, Canada, pp. 273–280, 2021.
[17]	M. X. Xu, Q. H. Zhou, H. M. Wu, et al., “PDMA: Probabilistic service migration approach for delay-aware and mobility-aware mobile edge computing,” Software:Practice and Experience, vol. 52, no. 2, pp. 394–414, 2022. doi: 10.1002/spe.3014
[18]	K. H. Chiang and N. Shenoy, “A 2-D random-walk mobility model for location-management studies in wireless networks,” IEEE Transactions on Vehicular Technology, vol. 53, no. 2, pp. 413–424, 2004. doi: 10.1109/TVT.2004.823544
[19]	X. L. Jiang, H. Shokri-Ghadikolaei, G. Fodor, et al., “Low-latency networking: Where latency lurks and how to tame it,” Proceedings of the IEEE, vol. 107, no. 2, pp. 280–306, 2019. doi: 10.1109/JPROC.2018.2863960
[20]	S. Pan, W. W. Zhou, Q. Q. Gu, et al., “Network selection algorithm based on spectral bandwidth mapping and an economic model in WLAN & LTE heterogeneous networks,” KSII Transactions on Internet and Information Systems, vol. 9, no. 1, pp. 68–86, 2015. doi: 10.3837/tiis.2015.01.005