NetKeeper: Enhancing Network Resilience with Autonomous Network Configuration Update on Traffic Patterns and Anomalies

Incremental policies and anomaly logs require operators to update network configuration during network operations. However, existing configuration methods lack the capability for intent understanding, traffic analysis optimization, and network dynamic adaptability, complicating overall configuration management.

We propose NetKeeper, an autonomous network configuration update framework. NetKeeper updates network configurations based on multimodal network intent comprising natural language input and anomaly logs, enabling adaptability to network dynamics and enhancing resilience through analyzing traffic patterns and anomalies. We implement northbound and southbound interfaces to translate network intents from operators and network management platforms respectively, bridging the gap between network intents and network behaviors. A multi-agent reinforcement learning model is designed for network configuration updates based on traffic patterns in dynamic networks. This model divides agents based on configuration parameter types, achieving both network resilience optimization and forwarding policy satisfaction.

Experiments in dynamic network show that NetKeeper updates network configurations with 99.6% average policy consistency, improves network performance by 5.3%, and reduces traffic shift by 8.7% on average.