Service Chain Orchestration Based on Deep Reinforcement Learning in Intent-Based IoT
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Intent-based network (IBN) is a novel approach to network management and automation designed to simplify a generic high-level policy called intent to a specific low-level network configuration. At present, plenty of researches have focused more on definition of intent-based northbound interface (NBI) but less on methods for intent-based service orchestration. In this paper, an IBN reference architecture is presented to manage IoT infrastructure and deliver end-to-end services across multi-domains. After that, this paper introduces a DDQN-based heuristic algorithm to solve the dynamic service chain orchestration problem. Simulation results clearly show that the proposed algorithm has better cost efficiency and convergence than those of compared algorithms, and can also guarantee the QoS requirements and make the traffic balanced.
KeywordsDeep reinforcement learning Intent-based Service chain IoT Orchestration
This work was supported by the science and technology project of Guangdong power grid (036000KK52160025).
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