Automatic guarantee scheme for intent-driven network slicing and reconfiguration

Commonly, the network configuration leans upon the operators' experience to operate network, including command-line configuration, middle-ware scripts, and troubleshooting. However, with the rise of neoteric B5G services, the manual way lacks flexibility and timeliness, resulting in an unsatisfactory level of configuration. It is necessary to consider a manual free configuration way for transport network. To cope with this problem, we present an intent-driven network architecture with self-adapting slicing policy and slices reconfiguration in an intent-orient manner. Aiming at intent request, intent analysis based on latent dirichlet allocation is introduced to establish the semantic graph to comprehend and enact the required slicing configuration language, namely intent translation. Then, in line with intent translation, we propose a self-adapted slicing policy generation and optimization base on deep reinforcement learning (SPG-RL) to find combined strategies that meet the intent requirements by dynamically integrating fine-grained slicing policies. Finally, deep neural evolution network (DNEN)-assisted model (SPG-RL-DNEN) is introduced to locate the incompatible slices at the millisecond level for slicing reconfiguration. When the network entropy reaches the threshold, SPG-RL-DNEN would reconfigure the incompatible slices for intent guarantee. The efficiency of our proposal are verified on enhanced SDN testbed.

Automated summary

With the rise of new B5G services, traditional manual network configuration lacks flexibility and timeliness, prompting the need for an intent-driven and self-adapting network architecture. This approach involves analyzing intent requests, utilizing deep reinforcement learning to optimize slicing policies, and employing deep neural evolution network for real-time slicing reconfiguration. The efficiency of this proposal has been verified on an enhanced SDN testbed.