Artificial Intelligence for Network Operations

The IETF draft titled "Artificial Intelligence (AI) for Network Operations" (https://datatracker.ietf.org/doc/draft-king-rokui-ainetops-usecases/) explores how AI and machine learning (ML) can be integrated into network operations—a concept referred to as AINetOps. The primary aim is to automate and optimize network management tasks, thereby improving efficiency, reliability, and scalability. This approach is relevant to both single-layer (IP or Optical) and multi-layer (IP/Optical) networks and is intended to tackle the growing complexity of modern network infrastructures.

AINetOps includes a broad set of use cases such as reactive troubleshooting, proactive assurance, closed-loop optimization, misconfiguration detection, and virtual operator support. By using AI and ML, networks can evolve from static, manually operated systems into dynamic environments capable of real-time adaptation and autonomous decision-making. This transformation enables predictive analytics, helping operators to detect and resolve issues before they affect service quality.

The draft highlights the need for existing IETF protocols and architectures to evolve in support of AINetOps. It outlines the architectural, procedural, and protocol-level changes required to implement AI-powered operations effectively. These include developing standardized interfaces and APIs, integrating AI engines with network components, and creating data models that accurately represent the network’s state and configuration.