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Adaptive Network Function Chain Orchestration Strategy For 6G Inclusive Intelligent Services
Ref: CISTER-TR-250101       Publication Date: 2025

Abstract:
In the upcoming 6G era, inclusive intelligent services (IIS) that rely on integrated communications and AI arithmetic will become the norm. These services require efficient distributed intelligent learning or reasoning. However, with the proliferation of complex applications, providing differentiated and customized services through effective Network Function Chain (NFC) orchestration has become a significant challenge. In this paper, an adaptive NFC orchestration strategy (ANFCOS) for 6G IIS is proposed to minimize costs. In particular, a tailored NFC orchestration is optimized, catering to diverse quality of service requirements. ANFCOS models this process as a Markov decision process and multi-actor-attention-critic (MAAC) reinforcement learning method for solving. The strategy enables the deployment and operational cost optimization of multiple parallel NFCs, relying on a dynamic attention-shifting mechanism. Additionally, We introduced a reinforcement learning framework based on maximum entropy to enhance the MAAC for solving and demonstrate its convergence. Simulation results show that ANFCOS significantly improves the network performance of 6G intelligent services. Compared with MADDPG, DDPG and MCTS, the cost of orchestration was reduced by 8.2%, 22.1% and 28.6%; the total delay of orchestration was reduced by 7.7%, 12.7% and 10.8%.

Yi Wen

,

Sai Zou

,

Yanglong Sun

,

Wei Ni

,

Kai Li

,

Hongfeng Gao

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