Reliability-assured service function chain migration strategy in edge networks using deep reinforcement learning

Li, Yilin1,2; Zhang, Peiying1,2; Kumar, Neeraj3; Guizani, Mohsen4; Wang, Jian5; Kostromitin, Konstantin Igorevich6; Wang, Yi7,8; Tan, Lizhuang2,9

2024-11-01

10.1016/j.jnca.2024.103999

JOURNAL OF NETWORK AND COMPUTER APPLICATIONS   影响因子和分区

1084-8045

1095-8592

With the widespread adoption of edge computing and the rollout of 5G technology, the edge network is experiencing rapid growth. Edge computing enables the execution of certain computational tasks on edge devices, fostering more efficient resource utilization. However, the reliability of the edge network is constrained by its network connections. Network instability can significantly compromise service quality. An effective service function chain (SFC) migration algorithm is essential to optimize resource utilization, enhance service quality. This paper begins by analyzing the current research landscape of edge networks and SFC migration algorithms. Subsequently, the challenges associated with edge network and SFC migration are formally articulated, leading to the proposal of a SFC migration algorithm based on deep reinforcement learning (DRL) with a focus on reliability assurance (RA-SFCM). The algorithm leverages multi-agent deep reinforcement learning to dynamically perceive changes in the edge network environment. It introduces an advantage function to evaluate the performance of each agent relative to the average level and incorporates a central attention mechanism with multiple attention heads to better capture the interdependencies and relationships among different agents. Additionally, this paper innovatively defines and quantifies the reliability of the migration process. By introducing a reliability penalty mechanism based on the migration target nodes and link capacity, it enhances the reliability of the migration schemes. The experimental results conclusively demonstrate the remarkable advantages of the RA-SFCM algorithm in terms of real-time performance, resource utilization efficiency, and reliability. Compared to algorithms such as Sa-VNFM, ROVM, and DLTSAC, RA-SFCM exhibits superior performance. For RA-SFCM, the optimized deployment migration strategy enhances realtime performance, precise resource management improves utilization efficiency, and advanced fault tolerance mechanisms strengthen reliability.

Edge network Network function virtualization Service function chain migration Reliability

SCI ; EI

英语

其他

Natural Science Foundation of Shandong Province, China["ZR20231ZH017","ZR2022LZH015","2023QF025"] ; Open Foundation of Key Laboratory of Computing Power Network and Information Security, Ministry of Education, Qilu University of Technology (Shandong Academy of Sciences) , China[2023ZD010] ; National Natural Science Foundation of China[62173345] ; Integrated Innovation of Science, Education and Industry of Qilu University of Technology (Shandong Academy of Sciences) , China[2023RCKY141] ; Russian Science Foundation (RSF)[22-71-10095] ; Major Key Project of Peng Cheng Laboratory[PCL2023A03] ; Guangdong High-Level Talents Special Support Program[2021TX05X205]

Computer Science

Computer Science, Hardware & Architecture ; Computer Science, Interdisciplinary Applications ; Computer Science, Software Engineering

WOS:001301126800001

ACADEMIC PRESS LTD- ELSEVIER SCIENCE LTD

COMPUTER SCIENCE

Web of Science

1.China Univ Petr East China, Qingdao Inst Software, Coll Comp Sci & Technol, Qingdao 266580, Peoples R China
2.Qilu Univ Technol, Key Lab Comp Power Network & Informat Secur, Natl Supercomp Ctr Jinan, Minist Educ,Shandong Comp Sci Ctr,Shandong Acad Sc, Jinan 250013, Peoples R China
3.Thapar Univ, Dept Comp Sci & Engn, Patiala 147004, India
4.Mohamed Bin Zayed Univ Artificial Intelligence, Machine Learning Dept, Abu Dhabi 999041, U Arab Emirates
5.China Univ Petr East China, Coll Sci, Qingdao 266580, Peoples R China
6.South Ural State Univ, Dept Phys Nanoscale Syst, Chelyabinsk 454080, Russia
7.Southern Univ Sci & Technol, Inst Future Networks, Shenzhen 518055, Peoples R China
8.Peng Cheng Lab, Shenzhen 518038, Peoples R China
9.Shandong Fundamental Res Ctr Comp Sci, Shandong Prov Key Lab Comp Networks, Jinan 250013, Peoples R China

Li, Yilin,Zhang, Peiying,Kumar, Neeraj,et al. Reliability-assured service function chain migration strategy in edge networks using deep reinforcement learning[J]. JOURNAL OF NETWORK AND COMPUTER APPLICATIONS,2024,231.

Li, Yilin.,Zhang, Peiying.,Kumar, Neeraj.,Guizani, Mohsen.,Wang, Jian.,...&Tan, Lizhuang.(2024).Reliability-assured service function chain migration strategy in edge networks using deep reinforcement learning.JOURNAL OF NETWORK AND COMPUTER APPLICATIONS,231.

Li, Yilin,et al."Reliability-assured service function chain migration strategy in edge networks using deep reinforcement learning".JOURNAL OF NETWORK AND COMPUTER APPLICATIONS 231(2024).