中文版 | English
Title

Efficient Dynamic Distributed Resource Slicing in 6G Multi-Access Edge Computing Networks with Online ADMM and Message Passing Graph Neural Networks

Author
Publication Years
2023
DOI
Source Title
ISSN
1536-1233
EISSN
1558-0660
VolumePPIssue:99Pages:1-18
Abstract
We consider the problem of resource slicing in the 6 generation multi-access edge computing (6G-MEC) network. The network includes many non-stationary space-air-ground-sea nodes with dynamic, unstable connections and resources, where any node can be in one of two hidden states: i) reliable – when the node generates/propagates no data errors; ii) unreliable – when the node can generate/propagate random errors. We show that solving this problem is challenging, since it represents a non-deterministic polynomial-time (NP) hard dynamic combinatorial optimization problem depending on the unknown distribution of hidden nodes' states and time-varying parameters (connections and resources of nodes) which can only be observed locally. To tackle these challenges, we develop a new deep learning (DL) model based on the message passing graph neural network (MPNN) to estimate hidden nodes' states in dynamic network environments. We then propose a novel algorithm based on the integration of MPNN-based DL and online alternating direction method of multipliers (ADMM) – extension of the well-known classical “static” ADMM to dynamic settings, where the slicing problem is solved distributedly, in real time, based on local information. We prove that our algorithm converges to a global optimum of our problem with a superior performance even in the highly-dynamic, unreliable scenarios.
Keywords
URL[Source Record]
Language
English
SUSTech Authorship
First
ESI Research Field
COMPUTER SCIENCE
Scopus EID
2-s2.0-85151489034
Data Source
Scopus
PDF urlhttps://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10083276
Citation statistics
Cited Times [WOS]:0
Document TypeJournal Article
Identifierhttp://kc.sustech.edu.cn/handle/2SGJ60CL/524271
DepartmentDepartment of Computer Science and Engineering
Affiliation
1.Department of Computer Science and Engineering, Southern University of Science and Technology, Shenzhen, China
2.School of Computer Science and Engineering, Nanyang Technological University, Singapore
3.Graduate School of Science and Engineering, Chitose Institute of Science and Technology, Chitose, Hokkaido, Japan
First Author AffilicationDepartment of Computer Science and Engineering
First Author's First AffilicationDepartment of Computer Science and Engineering
Recommended Citation
GB/T 7714
Asheralieva,Alia,Niyato,Dusit,Miyanaga,Yoshikazu. Efficient Dynamic Distributed Resource Slicing in 6G Multi-Access Edge Computing Networks with Online ADMM and Message Passing Graph Neural Networks[J]. IEEE Transactions on Mobile Computing,2023,PP(99):1-18.
APA
Asheralieva,Alia,Niyato,Dusit,&Miyanaga,Yoshikazu.(2023).Efficient Dynamic Distributed Resource Slicing in 6G Multi-Access Edge Computing Networks with Online ADMM and Message Passing Graph Neural Networks.IEEE Transactions on Mobile Computing,PP(99),1-18.
MLA
Asheralieva,Alia,et al."Efficient Dynamic Distributed Resource Slicing in 6G Multi-Access Edge Computing Networks with Online ADMM and Message Passing Graph Neural Networks".IEEE Transactions on Mobile Computing PP.99(2023):1-18.
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