Prediction based dynamic resource allocation method for edge computing first networking

doi:10.19682/j.cnki.1005-8885.2022.1010

中国邮电高校学报(英文) ›› 2023, Vol. 30 ›› Issue (3): 78-87.doi: 10.19682/j.cnki.1005-8885.2022.1010

Prediction based dynamic resource allocation method for edge computing first networking

Zhang Luying, Liu Xiaokai, Li Zhao, Xu Fangmin, Zhao Chenglin

1. School of Information and Communication Engineering, Beijing University of Posts and Telecommunications, Beijing 100876, China 2. Beijing Wulian Zhitong Technology Corporation, Beijing 100041, China 3. State Grid Liaoning Electric Power Supply Co. , Ltd. , Shenyang 110004, China

收稿日期:2021-09-16 修回日期:2023-04-07 出版日期:2023-06-30 发布日期:2023-06-30
通讯作者: 许方敏 E-mail:xufm@bupt.edu.cn
基金资助:
This work was supported by the National Natural Science Foundation of China ( 61971050 ) , and the Beijing Natural Science Foundation—Haidian Frontier Project ( L202017) .

Prediction based dynamic resource allocation method for edge computing first networking

Zhang Luying, Liu Xiaokai, Li Zhao, Xu Fangmin, Zhao Chenglin

1. School of Information and Communication Engineering, Beijing University of Posts and Telecommunications, Beijing 100876, China 2. Beijing Wulian Zhitong Technology Corporation, Beijing 100041, China 3. State Grid Liaoning Electric Power Supply Co. , Ltd. , Shenyang 110004, China

Received:2021-09-16 Revised:2023-04-07 Online:2023-06-30 Published:2023-06-30
Contact: Xu Fangmin E-mail:xufm@bupt.edu.cn
Supported by:
This work was supported by the National Natural Science Foundation of China ( 61971050 ) , and the Beijing Natural Science Foundation—Haidian Frontier Project ( L202017) .

摘要/Abstract

摘要：

Aiming at the factory with high-complex and multi-terminal in the industrial Internet of things ( IIoT), a hierarchical edge networking collaboration ( HENC ) framework based on the cloud-edge collaboration and computing first networking (CFN) is proposed to improve the capability of task processing with fixed computing resources on the edge effectively. To optimize the delay and energy consumption in HENC, a multi-objective optimization (MOO) problem is formulated. Furthermore, to improve the efficiency and reliability of the system, a resource prediction model based on ridge regression (RR) is proposed to forecast the task size of the next time slot, and an emergency-aware ( EA) computing resource allocation algorithm is proposed to reallocate tasks in edge CFN. Based on the simulation result, the EA algorithm is superior to the greedy resource allocation in time delay, energy consumption, quality of service (QoS) especially with limited computing resources.

关键词:

cloud-edge collaboration, computing first networking (CFN), computing resource allocation, multi-objective optimization (MOO)

Abstract: Aiming at the factory with high-complex and multi-terminal in the industrial Internet of things ( IIoT), a hierarchical edge networking collaboration ( HENC ) framework based on the cloud-edge collaboration and computing first networking (CFN) is proposed to improve the capability of task processing with fixed computing resources on the edge effectively. To optimize the delay and energy consumption in HENC, a multi-objective optimization (MOO) problem is formulated. Furthermore, to improve the efficiency and reliability of the system, a resource prediction model based on ridge regression (RR) is proposed to forecast the task size of the next time slot, and an emergency-aware ( EA) computing resource allocation algorithm is proposed to reallocate tasks in edge CFN. Based on the simulation result, the EA algorithm is superior to the greedy resource allocation in time delay, energy consumption, quality of service (QoS) especially with limited computing resources.

Key words: cloud-edge collaboration, computing first networking (CFN), computing resource allocation, multi-objective optimization (MOO)

中图分类号:

TP393.0

Zhang Luying, Liu Xiaokai, Li Zhao, Xu Fangmin, Zhao Chenglin. Prediction based dynamic resource allocation method for edge computing first networking[J]. The Journal of China Universities of Posts and Telecommunications, 2023, 30(3): 78-87.

参考文献

1. SHAMIM HOSSAIN S, MUHAMMAD G. Cloud-assisted industrial internet of things (IIoT) --Enabled framework for health monitoring. Computer Networks, 2016, 101: 192-202.

2. TSENG F H , WANG X F, CHOU L D , et al. Dynamic resource prediction and allocation for cloud data center using the multiobjective genetic algorithm. IEEE Systems Journal, 2018, 12(2): 1688-1699.

3. ZHANG H , CHEN S C, ZOU P, et al. Research and application of industrial equipment management service system based on cloud-edge collaboration. Proceedings of the 2019 Chinese Automation Congress (CAC’19), 2019, Nov 22-24, Hangzhou, China. Piscataway, NJ, USA: IEEE, 2019: 5451-5456.

4. Tian L , Yang M Z, Wang S G. An overview of compute first networking. International Journal of Web and Grid Services, 2021, 17(2): 81-97 .

5. ZHAO L J , TANG X Y, YOU Z P, et al. Operation and security considerations of federated learning platform based on compute first network. Proceedings of the 2020 IEEE/CIC International Conference on Communications in China (ICCC Workshops’20), 2020, Aug 9-11, Chongqing, China. Piscataway, NJ, USA: IEEE, 2020: 117-121.

6. SHEIKHAN M , MOHAMMADI N . Erratum to: Time series prediction using PSO-optimized neural network and hybrid feature selection algorithm for IEEE load data. Neural Computing and Applications, 2013, 23(3/4): 1185-1194.

7. CHEN Z Y, YANG L J, HUANG Y H, et al. PSO-GA based resource allocation strategy for cloud-based software services with workload-time windows. IEEE Access, 2020, 8: 151500-151510.

8. LUO S Q, CHEN X, WU Q , et al. HFEL: Joint edge association and resource allocation for cost-efficient hierarchical federated edge learning. IEEE Transactions on Wireless Communications, 2020, 19(10): 6535-6548.

9. GENG Y L, HU W J, YANG Y, et al. Energy-efficient computation offloading in cellular networks. Proceedings of the IEEE 23rd International Conference on Network Protocols (ICNP’15), 2015, Nov 10-13, San Francisco, CA, USA. Piscataway, NJ, USA: IEEE, 2015: 145-155.

10. YANG L, CAO J N, CHENG H, et al. Multi-user computation partitioning for latency sensitive mobile cloud applications. IEEE Transactions on Computers, 2015, 64(8): 2253-2266.

11. GUO S T, LIU J D, YANG Y Y, et al. Energy-efficient dynamic computation offloading and cooperative task scheduling in mobile cloud computing. IEEE Transactions on Mobile Computing, 2018, 18(2): 319-333.

12. CHEN M H, LIANG B, MIN D. Joint offloading and resource allocation for computation and communication in mobile cloud with computing access point. Proceedings of the 2017 IEEE Conference on Computer Communications (INFOCOM’17), 2017, May 1-4, Atlanta, GA, USA. Piscataway, NJ, USA: IEEE, 2017: 9p.

13. S. HU , G. LI, Dynamic Request Scheduling Optimization in Mobile Edge Computing for IoT Applications, IEEE Internet of Things Journal, 2020, 7(2) : 1426-1437.

14. TONG L, GAO W. Application-aware traffic scheduling for workload offloading in mobile clouds. Proceedings of the 35th Annual IEEE International Conference on Computer Communications (INFOCOM’16), 2016, Apr 10-14, San Francisco, CA, USA. Piscataway, NJ, USA: IEEE, 2016: 9p.

15. HU W J, CAO G H. Energy optimization through traffic aggregation in wireless networks. Proceedings of the 2014 IEEE Conference on Computer Communications (INFOCOM’14), 2014, Apr 27- May 2, Toronto, Canada. Piscataway, NJ, USA: IEEE, 2014: 916-924

16. SONG T, WANG Y L, LI G L, et al. Server consolidation energy-saving algorithm based on resource reservation and resource allocation strategy. IEEE Access, 2019, 7: 171452-171460.

17. WANG S G, GUO Y, ZHANG N, et al. Delay-aware microservice coordination in mobile edge computing: A reinforcement learning approach. IEEE Transactions on Mobile Computing, 2021, 20(3): 939-951.

18. LIU L M, ZHANG J, SONG S H, et al. Client-edge-cloud hierarchical federated learning. Proceedings of the 2020 IEEE International Conference on Communications (ICC’20), 2020, Jun 7-11, Dublin, Ireland. Piscataway, NJ, USA: IEEE, 2020: 6p.

19. WANG Z T, ZENG D Z, GU L, et al. A game-based network slicing and resource scheduling for compute first networking. Proceedings of the 2020 IEEE Global Communications Conference (GLOBECOM’20), 2020, Dec 7-11, Taipei, China. Piscataway, NJ, USA: IEEE, 2020: 6p.

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Prediction based dynamic resource allocation method for edge computing first networking

Prediction based dynamic resource allocation method for edge computing first networking

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