AI Network Configuration Patches for Closed-Loop Service Compliance
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Solution Overview
Problem
Conventional network management systems lack closed-loop correction and provenance control, relying on static templates and manual crosswalks, leading to inconsistent service quality and inefficiencies in heterogeneous networks.
Innovation Solution
A network control function (NCF) using transformer-based inference and federated updates generates schema-defined tokens, applies configuration patches validated against device grammar, and performs read-back telemetry to adapt network devices, ensuring minimal, device-specific corrections.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If conventional static templates and manual crosswalks are used for network management, then device configuration can be applied, but service quality consistency and management efficiency deteriorate
Solution Approach 1:
The system enables network devices to self-configure through automated generation of configuration patches by the transformer model, eliminating manual crosswalks and template-based approaches. The closed-loop system continuously monitors telemetry and self-corrects configuration deviations, allowing devices to adapt autonomously while maintaining service quality consistency across heterogeneous networks
Solution Approach 2:
The system dynamically changes configuration parameters based on real-time telemetry data and service targets. The transformer model generates optimized configuration patches that adjust multiple parameters simultaneously, enabling rapid adaptation to changing network conditions while maintaining consistency across diverse device types through unified parameter management
2Reliability
If full configuration reapplication is performed to correct service deviations, then service targets are restored, but network downtime increases
Solution Approach 1:
The system extracts and applies only the specific configuration parameters that deviate from service targets, rather than reapplying entire configuration sets. The transformer model identifies minimal corrective patches by comparing current telemetry against service targets, extracting only the necessary parameter changes to restore compliance while leaving other configurations unchanged
Solution Approach 2:
The system applies partial configuration updates rather than complete reconfiguration. By generating configuration patches that modify only the bounded subset of parameters needed to correct service deviations, the system achieves service target compliance with minimal disruption and reduced downtime compared to full configuration reapplication
3Measurement precision
If comprehensive network monitoring is implemented to ensure service quality, then service target compliance improves, but system complexity increases
Solution Approach 1:
The transformer model serves multiple functions: it generates initial configuration patches, monitors service target compliance through telemetry analysis, identifies deviations, and generates corrective patches. This multi-functional AI system consolidates what would otherwise require separate monitoring, analysis, and configuration management components, maintaining measurement precision while reducing overall system complexity
Solution Approach 2:
The transformer model acts as an intermediary between raw telemetry data and configuration decisions. It processes comprehensive monitoring data and translates it into targeted configuration patches, simplifying the relationship between extensive monitoring and complex configuration management by introducing an intelligent mediation layer that automatically correlates observations with corrective actions
Data Source
AI summary
Systems and methods for configuring and managing a network device with a transformer model under control of a network control function (NCF) are disclosed. A processor of the NCF receives a request that identifies a network management task and associated service targets. The processor forms a token set of schema-defined tokens that represent network context, applies positional encodings to generate an ordered token sequence, and invokes the transformer model to produce a configuration patch. The configuration patch is validated against a schema-constrained decoder that enforces device grammar and is applied to the target network device. Device state and telemetry are read back to obtain a read-back state, which is evaluated against the service targets. When telemetry deviates, the processor generates a further configuration patch that modifies a bounded subset of parameters relative to the read-back state to restore compliance