Priority-Aware Resource Allocation for VNF Deployment in Service Function Chains Based on Graph Reinforcement Learning  

作　　者：Seyha Ros Seungwoo Kang Taikuong Iv Inseok Song Prohim Tam Seokhoon Kim 

机构地区：[1]Department of Software Convergence,Soonchunhyang University,Asan,31538,Republic of Korea [2]School of Digital Technologies,American University of Phnom Penh,Phnom Penh,12106,Cambodia [3]Department of Computer Software Engineering,Soonchunhyang University,Asan,31538,Republic of Korea

出　　处：《Computers, Materials & Continua》2025年第5期1649-1665,共17页计算机、材料和连续体(英文)

基　　金：supported by Institute of Information&Communications Technology Planning and Evaluation(IITP)grant funded by the Korean government(MSIT)(No.RS-2022-00167197,Development of Intelligent 5G/6G Infrastructure Technology for the Smart City);in part by the National Research Foundation of Korea(NRF),Ministry of Education,through the Basic Science Research Program under Grant NRF-2020R1I1A3066543;in part by BK21 FOUR(Fostering Outstanding Universities for Research)under Grant 5199990914048;in part by the Soonchunhyang University Research Fund.

摘　　要：Recently,Network Functions Virtualization(NFV)has become a critical resource for optimizing capability utilization in the 5G/B5G era.NFV decomposes the network resource paradigm,demonstrating the efficient utilization of Network Functions(NFs)to enable configurable service priorities and resource demands.Telecommunications Service Providers(TSPs)face challenges in network utilization,as the vast amounts of data generated by the Internet of Things(IoT)overwhelm existing infrastructures.IoT applications,which generate massive volumes of diverse data and require real-time communication,contribute to bottlenecks and congestion.In this context,Multiaccess Edge Computing(MEC)is employed to support resource and priority-aware IoT applications by implementing Virtual Network Function(VNF)sequences within Service Function Chaining(SFC).This paper proposes the use of Deep Reinforcement Learning(DRL)combined with Graph Neural Networks(GNN)to enhance network processing,performance,and resource pooling capabilities.GNN facilitates feature extraction through Message-Passing Neural Network(MPNN)mechanisms.Together with DRL,Deep Q-Networks(DQN)are utilized to dynamically allocate resources based on IoT network priorities and demands.Our focus is on minimizing delay times for VNF instance execution,ensuring effective resource placement,and allocation in SFC deployments,offering flexibility to adapt to real-time changes in priority and workload.Simulation results demonstrate that our proposed scheme outperforms reference models in terms of reward,delay,delivery,service drop ratios,and average completion ratios,proving its potential for IoT applications.

关 键 词：Deep reinforcement learning graph neural network multi-access edge computing network functions virtualization software-defined networking 

分 类 号：TP393[自动化与计算机技术—计算机应用技术]