Network Function Virtualization(NFV) decouples network functions from hardware intermediate boxes, deploys function instances and arranges them into Service Function Chains(SFC) to realize network services.A multi-agent based group strategy deployment method is proposed for the dynamic deployment of SFC in large-scale network environments with resource constraints.The proposed method combines the advantages of centralized Deep Reinforcement Learning(DRL) and traditional distributed methods.The SFC deployment problem is modeled as a Partially Observable Markov Decision Process(POMDP), with each node deploying an Actor-Critic(AC) agent.The global training strategy can be obtained only by observing local node information, which has DRL flexibility and adaptability. The local agent controls the interaction process to solve complex control and slow response speed problems in large-scale networks using centralized DRL methods.Based on the multithreading concept, this research aims to collect and integrate the experience of each node for centralized training, to avoid problems such as insufficient training and policy inapplicability caused by low request traffic in some nodes during the fully distributed training process. Experimental results demonstrate that while it adapts well to complex and everchanging environments in practice, it is not necessary for the proposed method to rely on specific scenarios or to consider network scale.In relatively complex traffic environments, compared with CDRL and GCASP methods, the proposed method's deployment success rate in multiple traffic modes increased by over 20%, while reducing deployment costs.
Network Function Virtualization(NFV),
Service Function Chain(SFC),
Deep Reinforcement Learning(DRL),
Partially Observable Markov Decision Process(POMDP),