Intelligent Service Function Chain Mapping Framework for Cloud-and-Edge-Collaborative IoT

doi:10.19682/j.cnki.1005-8885.2021.1018

中国邮电高校学报(英文) ›› 2022, Vol. 29 ›› Issue (3): 54-68.doi: 10.19682/j.cnki.1005-8885.2021.1018

Intelligent Service Function Chain Mapping Framework for Cloud-and-Edge-Collaborative IoT

1. Information and Communication Branch, State Grid Liaoning Electric Power Co. , Ltd. , Shenyang 110004, China
2. State Key Laboratory of Networking and Switching Technology, Beijing University of Posts and Telecommunications, Beijing 100876, China
3. State Grid Liaoning Electric Power Research Institute, Shenyang 110004, China

收稿日期:2021-03-01 修回日期:2021-10-02 出版日期:2022-06-30 发布日期:2022-06-30
通讯作者: Xu Siya E-mail:xusiyaxsy@hotmail.com
基金资助:
This work was supported by the Science and Technology Project of State Grid Corporation of China
(SGLNXT00GCJS2000160)

Intelligent Service Function Chain Mapping Framework for Cloud-and-Edge-Collaborative IoT

1. Information and Communication Branch, State Grid Liaoning Electric Power Co. , Ltd. , Shenyang 110004, China
2. State Key Laboratory of Networking and Switching Technology, Beijing University of Posts and Telecommunications, Beijing 100876, China
3. State Grid Liaoning Electric Power Research Institute, Shenyang 110004, China

Received:2021-03-01 Revised:2021-10-02 Online:2022-06-30 Published:2022-06-30
Contact: Xu Siya E-mail:xusiyaxsy@hotmail.com
Supported by:
This work was supported by the Science and Technology Project of State Grid Corporation of China
(SGLNXT00GCJS2000160)

摘要/Abstract

摘要：

With the rapid development of Internet of thing (IoT) technology, it has become a challenge to deal with the increasing number and diverse requirements of IoT services. By combining burgeoning network function virtualization ( NFV) technology with cloud computing and mobile edge computing ( MEC), an NFV-enabled cloud-and-edge-collaborative IoT (CECIoT) architecture can efficiently provide flexible service for IoT traffic in the form of a service function chain (SFC) by jointly utilizing edge and cloud resources. In this promising architecture, a difficult issue is how to balance the consumption of resource and energy in SFC mapping. To overcome this challenge, an intelligent energy-and-resource-balanced SFC mapping scheme is designed in this paper. It takes the comprehensive deployment consumption as the optimization goal, and applies a deep Q-learning(DQL)-based SFC mapping (DQLBM) algorithm as well as an energy-based topology adjustment (EBTA) strategy to make efficient use of the limited network resources, while satisfying the delay requirement of users. Simulation results show that the proposed scheme can decrease service delay, as well as energy and resource consumption.

关键词: Internet of thing (IoT), mobile edge computing, network function virtualization (NFV), deep Q-learning (DQL)

Abstract: With the rapid development of Internet of thing (IoT) technology, it has become a challenge to deal with the increasing number and diverse requirements of IoT services. By combining burgeoning network function virtualization ( NFV) technology with cloud computing and mobile edge computing ( MEC), an NFV-enabled cloud-and-edge-collaborative IoT (CECIoT) architecture can efficiently provide flexible service for IoT traffic in the form of a service function chain (SFC) by jointly utilizing edge and cloud resources. In this promising architecture, a difficult issue is how to balance the consumption of resource and energy in SFC mapping. To overcome this challenge, an intelligent energy-and-resource-balanced SFC mapping scheme is designed in this paper. It takes the comprehensive deployment consumption as the optimization goal, and applies a deep Q-learning(DQL)-based SFC mapping (DQLBM) algorithm as well as an energy-based topology adjustment (EBTA) strategy to make efficient use of the limited network resources, while satisfying the delay requirement of users. Simulation results show that the proposed scheme can decrease service delay, as well as energy and resource consumption.

Key words: Internet of thing (IoT), mobile edge computing, network function virtualization (NFV), deep Q-learning (DQL)

Yang Chao, Li Yimin, Li Tong, Xu Siya, Qi Jun, Zhang Yu. Intelligent Service Function Chain Mapping Framework for Cloud-and-Edge-Collaborative IoT[J]. The Journal of China Universities of Posts and Telecommunications, 2022, 29(3): 54-68.

参考文献

1. SARRIGIANNIS I, RAMANTAS K, KARTSAKLI E, et al. Online VNF lifecycle management in an MEC-enabled 5G IoT architecture. IEEE Internet of Things Journal, 2020, 7(5): 4183-4194.

2. LEIVADEAS A, KESIDIS G, IBNKAHLA M, et al. VNF placement optimization at the edge and cloud. Future Internet, 2019, 11(3): 69-89.

3. HE W C, GUO S Y, LIANG Y, et al. Markov approximation method for optimal service orchestration in iot network. IEEE Access, 2019, 7: 49538-49548.

4. SUN G, ZHOU R, SUN J, et al. Energy-efficient provisioning for service function chains to support delay-sensitive applications in network function virtualization. IEEE Internet of Things Journal, 2020, 7(7): 6116-6131.

5. ARULKUMARAN K, DEISENROTH M P, BRUNDAGE M, et al. Deep reinforcement learning: A brief survey. IEEE Signal Processing Magazine, 2017, 34(6): 26-38.

6. Jemaa F B, Pujolle G, Pariente M. QoS-aware VNF placement optimization in edge-central carrier cloud architecture. Proceedings of the 2016 IEEE Global Communications Conference (GLOBECOM’16), 2016, Dec 4-8, Washington, DC, USA. Piscataway, NJ, USA: IEEE, 2016: 7p.

7. VAN LINGEN F, YANNUZI M, JAIN A, et al. The unavoidable convergence of NFV, 5G, and fog: A model-driven approach to bridge cloud and edge. IEEE Communications Magazine, 2017, 55(8): 28-35.

8. WANG Y X, LU P, LU W, et al. Cost-efficient virtual network function graph (vNFG) provisioning in multidomain elastic optical networks. Journal of Lightwave Technology, 2017, 35(13): 2712-2723.

9. KUO T W, LIOU B H, LIN K C J, et al. Deploying chains of virtual network functions: On the relation between link and server usage. IEEE/ACM Transactions on Networking, 2018, 26(4): 1562-1576.

10. KAR B, WU E H, LIN Y, et al. Energy cost optimization in dynamic placement of virtualized network function chains. IEEE Transactions on Network and Service Management, 2018, 15(1): 372-386.

11. KOUAH R, ALLEG A, LARABA A, et al. Energy-aware placement for IoT-service function chain. Proceedings of the IEEE 23rd International Workshop on Computer Aided Modeling and Design of Communication Links and Networks (CAMAD’18), 2018, Sept 17-19, Barcelona, Spain. Piscataway, NJ, USA: IEEE, 2018: 7p.

12. SUN G, LI Y Y, YU H F, et al. Energy-efficient and traffic-aware service function chaining orchestration in multi-domain networks. Future Generation Computer Systems, 2019, 91: 347-360.

13. CHEN G, HE W, LIU J, et al. Energy-aware server provisioning and load dispatching for connection-intensive Internet services. Proceedings of the 5th USENIX Symposium on Networked Systems Design and Implementation (NSDI'08), 2008, Apr 16-18, San Francisco, CA, USA. Berkeley, CA, USA: USENIX Association, 2008: 337-350.

14. SHAW R, HOWLEY E, BARRETE E, et al. An energy efficient anti-correlated virtual machine placement algorithm using resource usage predictions. Simulation Modelling Practice and Theory, 2019, 93: 322-342.

15. QIAN H Y, MEDHI D. Server operational cost optimization for cloud computing service providers over a time horizon. Proceedings of the 11th USENIX Workshop on Hot Topics in Management of Internet, Cloud, and Enterprise Networks and Services (Hot-ICE’11), 2011, Mar 29, Boston, MA, USA. Berkeley, CA, USA: USENIX Association, 2011: 4p.

16. TRAN N H, TRAN D H, REN S, et al. How geo-distributed data centers do demand response: A game theoretic approach. IEEE Transactions on Smart Grid, 2016, 7(2): 937-947.

17. FU X Y, YU F R, WANG J Y, et al. Dynamic service function chain embedding for NFV-enabled IoT: A deep reinforcement learning approach. IEEE Transactions on Wireless Communications, 2020, 19(1): 507-519.

18. OUYANG C, WEI Y K, LENG S, et al. Service chain performance optimization based on middlebox deployment. Proceedings of the IEEE 17th International Conference on Communication Technology (ICCT’17), 2017, Oct 27-30, Chengdu, China. Piscataway, NJ, USA: IEEE, 2017: 1947-1952.

19. HEGYI A, FLINCK H, KETYKO I, et al. Application orchestration in mobile edge cloud: Placing of iot applications to the edge. Proceedings of the IEEE 1st International Workshops on Foundations and Applications of Self* Systems (FAS*W’16), 2016, Sept 12-16, Augsburg, Germany. Piscataway, NJ, USA: IEEE, 2016: 230-235.

20. NAM Y, SONG S, CHUNG J M, et al. Clustered NFV service chaining optimization in mobile edge clouds. IEEE Communications Letters, 2017, 21(2): 350-353.

21. FADLULLAH Z M, TANG F, MAO B, et al. On intelligent traffic control for large-scale heterogeneous networks: A value matrix-based deep learning approach. IEEE Communications Letters, 2018, 22(12): 2479-2482.

22. ZHAO L, WANG J D, LIU J J, et al. Routing for crowd management in smart cities: A deep reinforcement learning perspective. IEEE Communications Magazine, 2019, 57(4): 88-93.

23. QUANG P T A, HADJADJ-AOUL Y, OUTTAGARTS A. A deep reinforcement learning approach for VNF forwarding graph embedding. IEEE Transactions on Network and Service Management, 2019, 16(4): 1318-1331.

24. GUO S Y, DAI Y, XU S Y, et al. Trusted cloud-edge network resource management: DRL-driven service function chain orchestration for IoT. IEEE Internet of Things Journal, 2020, 7(7): 6010-6022.

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Intelligent Service Function Chain Mapping Framework for Cloud-and-Edge-Collaborative IoT

Intelligent Service Function Chain Mapping Framework for Cloud-and-Edge-Collaborative IoT

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