Automation of Intent-based Service Operation with Models and AI/ML
Takayuki Kuroda, Yutaka Yakuwa, Takashi Maruyama, Takuya Kuwahara, Kozo Satoda · NOMS 2022-2022 IEEE/IFIP Network Operations and Management Symposium · 2022
To achieve digital transformation, it is becoming more important to flexibly and quickly provide information and communication technology (ICT) services according to various business requirements. Closed-loop control and its automation with artificial intelligence/machine learning (AI/ML) is a promising approach. However, most of such efforts focus on monitoring and analysis of resources. The challenges remain in automating operation of an entire service that consists of many resources in accordance with the results of the monitoring and analysis. We propose a scheme for automatically designing the topology and configuration of an entire service with various functional/non-functional requirements. To increase both the flexibility of the requirements and quickness to design, our scheme combines a model-based approach with AI/ML. It generates a huge number of design candidates through the model-based approach, and selects promising candidates efficiently by using AI/ML. We present the architecture of the proposed scheme and experiments we conducted involving a case study. Through the experiment, we confirmed that (1) the design speed was improved by 6 times autonomously, and (2) the increasing speed of design time with respect to the increase of intent size is significantly suppressed.