A Service Function Chain deployment Algorithm Based on Proximal Policy Optimization

YAN Zhi; YU Huailong; OUYANG Bo; WANG Yaonan

doi:10.11999/JEIT230902

Volume 46 Issue 7

Jul. 2024

Turn off MathJax

Article Contents

Article Navigation > Journal of Electronics & Information Technology > 2024 > 46(7): 2869-2878

YAN Zhi, YU Huailong, OUYANG Bo, WANG Yaonan. A Service Function Chain deployment Algorithm Based on Proximal Policy Optimization[J]. Journal of Electronics & Information Technology, 2024, 46(7): 2869-2878. doi: 10.11999/JEIT230902

Citation:

YAN Zhi, YU Huailong, OUYANG Bo, WANG Yaonan. A Service Function Chain deployment Algorithm Based on Proximal Policy Optimization[J]. Journal of Electronics & Information Technology, 2024, 46(7): 2869-2878. doi: 10.11999/JEIT230902

Citation:

PDF( 5420 KB)

A Service Function Chain deployment Algorithm Based on Proximal Policy Optimization

doi: 10.11999/JEIT230902

College of Electrical and Information Engineering, Hunan University, Changsha 410082, China

Funds: The National Key Research and Development Program of China (2021YFC1910402), The Major Program of the National Natural Science Foundation of China (62293511), The National Science and Technology Major Project of the Ministry of Science and Technology of Hunan Province, China (2021GK1010), The Program of The State Key Laboratory of Networking and Switching Technology, Beijing University of Posts and Telecommunications, China (SKLNST-2021-2-03)

Received Date: 2024-08-16
Rev Recd Date: 2024-05-13

Available Online: 2024-05-22

Publish Date: 2024-07-29

Abstract

Abstract

In order to solve the high-dimensional Service Function Chain (SFC) deployment problem of high reliability and low cost in the Network Function Virtualization (NFV) environment, an Improving Service and Reducing Consumption based on Proximal Policy Optimization (PPO-ISRC) is proposed. Firstly, considering the characteristics of the underlying physical server and SFC, the state transition process of the underlying server network is descried, and the deployment of SFC is taken as a Markov Decision Process. Then the reward function is set with the optimization goal of maximizing the service rate and minimizing resource consumption. Finally the PPO method is used to solve the SFC deployment strategy. The results show that compared with the heuristic algorithm First-Fit Dijkstra (FFD) and the Deep Deterministic Policy Gradient (DDPG) algorithm, the proposed algorithm has the characteristics of fast convergence speed and higher stability. Under the requirements of service quality, the deployment cost is reduced and the reliability of network service is improved.
- Network Function Virtualization(NFV),
- Service Function Chain(SFC),
- Deep Reinforcement Learning(DRL),
- Proximal Policy Optimization(PPO)

FullText(HTML)

References(20)

References

[1]	李鹤, 张恒升, 朱瑾瑜, 等. 5G专网融合时间敏感网络架构技术[J]. 移动通信, 2022, 46(8): 30–35. doi: 10.3969/j.issn.1006-1010.2022.08.006. LI He, ZHANG Hengsheng, ZHU Jinyu, et al. Research on the fusion architecture technology between 5G private network and time-sensitive network[J]. Mobile Communications, 2022, 46(8): 30–35. doi: 10.3969/j.issn.1006-1010.2022.08.006.
[2]	MATENCIO-ESCOLAR A, WANG Qi, and CALERO J M A. SliceNetVSwitch: Definition, design and implementation of 5G multi-tenant network slicing in software data paths[J]. IEEE Transactions on Network and Service Management, 2020, 17(4): 2212–2225. doi: 10.1109/TNSM.2020.3029653.
[3]	唐伦, 王恺, 张月, 等. 网络切片场景下基于分布式生成对抗网络的服务功能链异常检测[J]. 电子与信息学报, 2023, 45(1): 262–271. doi: 10.11999/JEIT211261. TANG Lun, WANG Kai, ZHANG Yue, et al. Service function chain anomaly detection based on distributed generative adversarial network in network slicing scenario[J] Journal of Electronics & Information Technology, 2023, 45(1): 262–271. doi: 10.11999/JEIT211261.
[4]	张岳, 张俊楠, 吴晓春, 等. 基于改进灰狼优化算法的服务功能链映射算法[J]. 电信科学, 2022, 38(11): 57–72. doi: 10.11959/j.issn.1000-0801.2022275. ZHANG Yue, ZHANG Junnan, WU Xiaochun, et al. Improved grey wolf optimization algorithm based service function chain mapping algorithm[J]. Telecommunications Science, 2022, 38(11): 57–72. doi: 10.11959/j.issn.1000-0801.2022275.
[5]	高媛, 方海, 赵扬, 等. 基于自然梯度Actor-Critic强化学习的卫星边缘网络服务功能链部署方法[J]. 电子与信息学报, 2023, 45(2): 455–463. doi: 10.11999/JEIT211384. GAO Yuan, FANG Hai, ZHAO Yang, et al. A satellite edge network service function chain deployment method based on natural gradient actor-critic reinforcement learning[J]. Journal of Electronics & Information Technology, 2023, 45(2): 455–463. doi: 10.11999/JEIT211384.
[6]	BARI F, CHOWDHURY S R, AHMED R, et al. Orchestrating virtualized network functions[J]. IEEE Transactions on Network and Service Management, 2016, 13(4): 725–739. doi: 10.1109/TNSM.2016.2569020.
[7]	SUN Quanying, LU Ping, LU Wei, et al. Forecast-assisted NFV service chain deployment based on affiliation-aware vNF placement[C]. 2016 IEEE Global Communications Conference (GLOBECOM), Washington, USA, 2016: 1–6. doi: 10.1109/GLOCOM.2016.7841846.
[8]	ZHANG Qixia, XIAO Yikai, LIU Fangming, et al. Joint optimization of chain placement and request scheduling for network function virtualization[C]. 2017 IEEE 37th International Conference on Distributed Computing Systems (ICDCS), Atlanta, USA, 2017: 731–741. doi: 10.1109/ICDCS.2017.232.
[9]	QU Long, ASSI C, and SHABAN K. Delay-aware scheduling and resource optimization with network function virtualization[J]. IEEE Transactions on Communications, 2016, 64(9): 3746–3758. doi: 10.1109/TCOMM.2016.2580150.
[10]	BECK M T and BOTERO J F. Scalable and coordinated allocation of service function chains[J]. Computer Communications, 2017, 102: 78–88. doi: 10.1016/j.comcom.2016.09.010.
[11]	SINGH S, OKUN A, and JACKSON A. Learning to play go from scratch[J]. Nature, 2017, 550(7676): 336–337. doi: 10.1038/550336a.
[12]	ZHU Yuchao, YAO Haipeng, MAI Tianle, et al. Multiagent reinforcement-learning-aided service function chain deployment for internet of things[J]. IEEE Internet of Things Journal, 2022, 9(17): 15674–15684. doi: 10.1109/JIOT.2022.3151134.
[13]	XIAO Yikai, ZHANG Qixia, LIU Fangming, et al. NFVdeep: Adaptive online service function chain deployment with deep reinforcement learning[C]. International Symposium on Quality of Service, Phoenix, USA, 2019: 21. doi: 10.1145/3326285.3329056.
[14]	TOUMI N, BAGAA M, and KSENTINI A. On using deep reinforcement learning for multi-domain SFC placement[C]. 2021 IEEE Global Communications Conference (GLOBECOM), Madrid, Spain, 2021: 1–6. doi: 10.1109/GLOBECOM46510.2021.9685367.
[15]	SCHULMAN J, WOLSKI F, DHARIWAL P, et al. Proximal policy optimization algorithms[EB/OL]. https://arxiv.org/abs/1707.06347, 2017.
[16]	IMT-2020(5G)推进组. 5G核心网云化部署需求与关键技术白皮书[R]. 北京: IMT-2020(5G)推进组, 2018. IMT-2020(5G) the Promotion Group. The white paper of 5G core network cloud deployment requirements and key technologies[R]. Beijing: IMT-2020(5G) the Promotion Group, 2018.
[17]	JALALITABAR M, GULER E, ZHENG Danyang, et al. Embedding dependence-aware service function chains[J]. Journal of Optical Communications and Networking, 2018, 10(8): C64–C74. doi: 10.1364/JOCN.10.000C64.
[18]	ZHANG Tao, XU Changqiao, ZHANG Bingchi, et al. Towards attack-resistant service function chain migration: A model-based adaptive proximal policy optimization approach[J]. IEEE Transactions on Dependable and Secure Computing, 2023, 20(6): 4913–4927. doi: 10.1109/TDSC.2023.3237604.
[19]	HUANG Bin and WANG Jianhui. Deep-reinforcement-learning-based capacity scheduling for PV-battery storage system[J]. IEEE Transactions on Smart Grid, 2021, 12(3): 2272–2283. doi: 10.1109/TSG.2020.3047890.
[20]	YALA L, FRANGOUDIS P A, LUCARELLI G, et al. Cost and availability aware resource allocation and virtual function placement for CDNaaS provision[J]. IEEE Transactions on Network and Service Management, 2018, 15(4): 1334–1348. doi: 10.1109/TNSM.2018.2874524.