A heterogeneous graph neural network assisted multi-agent reinforcement learning for parallel service function chain deployment

The deployment of parallel Service Function Chains (SFCs) in Network Function Virtualization (NFV) environments presents significant challenges in jointly optimizing Virtual Network Function (VNF) parallelization and placement decisions. Traditional approaches typically decouple these decisions, lea...

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Vydané v:	Journal of King Saud University. Computer and information sciences Ročník 37; číslo 8; s. 236 - 22
Hlavní autori:	Ai, Yintan, Li, Hua, Ruan, Hongwei, Liu, Hanlin
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	Cham Springer International Publishing 01.10.2025 Springer Nature B.V Springer
Predmet:	Complexity Computer Imaging Computer Science Database Management Decisions Deep learning Effectiveness Efficiency Flexibility Graph neural networks Graph representations Graphical representations Heterogeneous graph neural network Machine Learning Multi-agent reinforcement learning Multiagent systems Network function virtualization Network topologies Neural networks Optimization Original Paper Parallelized service function chain Pattern Recognition and Graphics Placement Polynomials Resource utilization Software Software Engineering/Programming and Operating Systems Systems and Data Security Theory of Computation Traffic Virtual network functions Virtual networks Vision Multi-agent reinforcement learning Virtual network functions Heterogeneous graph neural network Parallelized service function chain
ISSN:	1319-1578, 2213-1248, 1319-1578
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Popis
Shrnutí:	The deployment of parallel Service Function Chains (SFCs) in Network Function Virtualization (NFV) environments presents significant challenges in jointly optimizing Virtual Network Function (VNF) parallelization and placement decisions. Traditional approaches typically decouple these decisions, leading to suboptimal performance and inefficient resource utilization. This paper proposes HGNN-PSFC, a novel heterogeneous graph neural network-assisted multi-agent deep reinforcement learning framework that jointly optimizes VNF parallelization and placement for parallel SFC deployment. Our approach employs cooperative agents: a Parallelization Agent that determines optimal VNF parallelization structures, and multiple Placement Agents that make VNF placement decisions. The framework utilizes a heterogeneous graph representation to capture complex relationships between VNFs, substrate network topology, and current VNF placement states. Through Multi-Agent Proximal Policy Optimization (MAPPO) training within a Centralized Training with Decentralized Execution (CTDE) paradigm, our method achieves effective coordination between parallelization and placement decisions. Extensive experimental results demonstrate that HGNN-PSFC achieves near-optimal performance with approximately 92% of the optimal algorithm’s effectiveness while maintaining polynomial computational complexity.
Bibliografia:	ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 14
ISSN:	1319-1578 2213-1248 1319-1578
DOI:	10.1007/s44443-025-00258-1