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

Asheralieva，Alia1; Niyato，Dusit2; Miyanaga，Yoshikazu3

2023

10.1109/TMC.2023.3262514

IEEE Transactions on Mobile Computing   影响因子和分区

1536-1233

1558-0660

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.

6G 6G mobile communication deep learning dynamic combinatorial optimization Dynamic scheduling graph neural networks graph theory Heuristic algorithms mathematical decomposition multi-access edge computing network slicing Network topology Optimization Quality of service Reliability space-air-ground-sea networks ultra-reliable low-latency communications

SCI ; EI

英语

第一

20231413850849

Combinatorial optimization ; Computation theory ; Deep learning ; Graph neural networks ; Graph theory ; Heuristic algorithms ; Message passing ; Polynomial approximation ; Random errors

Ergonomics and Human Factors Engineering:461.4 ; Computer Theory, Includes Formal Logic, Automata Theory, Switching Theory, Programming Theory:721.1 ; Computer Programming:723.1 ; Data Processing and Image Processing:723.2 ; Artificial Intelligence:723.4 ; Combinatorial Mathematics, Includes Graph Theory, Set Theory:921.4 ; Optimization Techniques:921.5 ; Numerical Methods:921.6

COMPUTER SCIENCE

2-s2.0-85151489034

Scopus

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

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.

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.

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.