Network Controller Graph Model for Intent Provisioning
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Solution Overview
Problem
Current network management systems face challenges in efficiently configuring and managing network devices, particularly in responding to degraded resources and intent changes, which can lead to service disruptions and impact service-level agreements (SLAs).
Innovation Solution
A network management system that uses a graph model to represent intent data, allowing for parallel and incremental compilation and extensibility, determines degraded stateful intents, provisions new resources, and compiles successful intents into low-level configuration data to configure network devices, while also managing stateless intents by maintaining both deployed and undeployed graph models to ensure consistent and concurrent updates.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Speed
If network management systems use traditional sequential compilation methods for intent data models, then system complexity is reduced, but response time to resource degradation and service disruption increases
Solution Approach 1:
The compilation process is divided into independent parallel tasks that can execute simultaneously. Each intent node in the graph model can be compiled independently, allowing the system to process multiple configuration changes concurrently rather than sequentially, thereby reducing response time while maintaining manageable complexity through modular task division
Solution Approach 2:
The system performs preliminary compilation of intent data models before actual resource allocation. By pre-compiling the graph model and maintaining it in a ready state, the system can rapidly respond to resource degradation events without needing to perform full compilation during critical moments, thus improving response speed
2Reliability
If the system maintains both deployed and undeployed graph models for concurrent updates, then update consistency and availability are improved, but memory consumption and processing overhead increase
Solution Approach 1:
The graph model is segmented into deployed and undeployed versions, allowing the system to maintain only the necessary portions of data in memory at any given time. The undeployed model serves as a buffer for upcoming changes while the deployed model remains active, enabling efficient memory management while ensuring update consistency
Solution Approach 2:
The system dynamically switches between deployed and undeployed graph models based on operational needs. When updates are ready, the system transitions from the current deployed model to the prepared undeployed model, allowing flexible memory allocation and reducing the need to maintain both full models simultaneously
3Manufacturing precision
If the system performs full reconfiguration of network devices for each intent change, then configuration accuracy is ensured, but service disruption time increases
Solution Approach 1:
The configuration update process is segmented into incremental changes rather than full reconfiguration. By identifying and applying only the specific configuration changes needed for each intent node update, the system maintains configuration accuracy for affected areas while minimizing overall disruption time through targeted rather than comprehensive updates
Solution Approach 2:
The system performs preliminary validation and preparation of configuration changes before applying them to network devices. By pre-validating intent data models and preparing configuration commands in advance, the system ensures configuration accuracy is maintained while reducing the actual application time and service disruption
Data Source
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AI summary
A controller device manages a plurality of network devices. The controller device includes one or more processing units implemented in circuitry and configured to determine that one or more stateful intents used to manage the plurality of network devices and represented by a graph model are degraded due to assigned resources for the stateful intents having become degraded; in response to determining that the one or more stateful intents are degraded, determine resources for the stateful intents, the resources corresponding to vertices of the graph model; provision the stateful intents using the determined resources; determine whether the provisioning of the stateful intents was successful; compile at least one of the stateful intents that was successful into low-level configuration data for at least one network device of the plurality of network devices; and configure the at least one network device using the low-level configuration data.