Intent-Driven Network Change Workflows for Dependency-Safe Updates
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Network changes and updates, such as software or firmware updates, require manual creation of complex and error-prone 'recipes' that account for unique network dependencies, leading to potential failures and incompatibilities.
Innovation Solution
An automated network change system that generates a workflow of changes as a directed graph, using intents to describe network states and compute ordered operations, allowing parallel execution and minimizing human error.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If manual creation of change recipes is performed, then flexibility to handle unique network dependencies is improved, but error rate and time consumption increase
Solution Approach 1:
The system performs self-service by automatically analyzing network topology, identifying dependencies, and generating change recipes without manual intervention. The automated dependency analysis engine scans the network configuration, builds a dependency graph, and computes optimal change sequences, eliminating human errors while maintaining adaptability to unique network structures.
Solution Approach 2:
The patent replaces the manual mechanical process of creating change recipes with an automated computational system. The dependency analysis engine uses algorithmic approaches to scan network configurations, identify relationships between network elements, and generate change sequences, substituting human operators with automated software that eliminates errors while handling complex dependencies.
2Adaptability or versatility
If manual creation of change recipes is performed, then customization for specific networks is improved, but time consumption increases
Solution Approach 1:
The system automatically performs dependency analysis and change recipe generation without requiring manual operator involvement. The automated engine scans network configurations, identifies dependencies, and computes optimal change sequences instantaneously, reducing time consumption from hours or days of manual work to minutes or seconds of automated processing.
Solution Approach 2:
The patent replaces time-consuming manual analysis and recipe creation with automated computational processes. The dependency analysis engine uses algorithms to rapidly scan network configurations, build dependency graphs, and generate customized change sequences, reducing the time required from manual hours to automated seconds while maintaining full customization for specific network topologies.
3Reliability
If automated network changes are implemented, then consistency and reliability are improved, but complexity of the system increases
Solution Approach 1:
The automated system is segmented into distinct functional modules: network configuration scanner, dependency graph builder, change sequence calculator, and recipe generator. Each module performs a specific task, making the overall complex system manageable through modular design. This segmentation allows the system to achieve high consistency and reliability while keeping the complexity organized and maintainable.
Solution Approach 2:
The patent introduces an intermediary dependency analysis engine that acts as a mediator between network configuration and change implementation. This intermediary component automatically analyzes dependencies, computes optimal sequences, and generates change recipes, shielding operators from system complexity while ensuring consistent and reliable change implementation across diverse network topologies.
4Adaptability or versatility
If multiple operators are involved in network changes, then diverse expertise is improved, but likelihood of contradictory changes increases
Solution Approach 1:
The patent merges the expertise of multiple operators into a single automated dependency analysis engine. The system consolidates knowledge about network dependencies, compatibility constraints, and optimal change sequences into a unified algorithmic approach, eliminating contradictory changes that arise from multiple human operators while preserving the benefits of diverse expertise through comprehensive automated analysis.
Solution Approach 2:
The system implements feedback mechanisms where the automated engine continuously validates change sequences against network dependencies and compatibility constraints. Before implementing changes, the system verifies that each operation is compatible with previously applied changes and future planned changes, providing feedback loops that prevent contradictory changes while allowing diverse expertise to be incorporated into the automated decision-making process.
Data Source
Figure 1
Figure 2~3
Figure 4
AI summary
Methods, systems, and apparatus, for automatically changing a network system. A method includes receiving a set of first intents that describe a state of a first switch fabric; receiving a set of second intents that describe a state of a second switch fabric; computing a set of network operations to perform on the first switch fabric to achieve the second switch fabric, the set of operations also defining an order in which the operations are to be executed, and the set of operations determined based on the set of first intents, the set of second intents, and migration logic that defines a ruleset for selecting the operations based on the set of first intents and the second intents; and executing the set of network operations according to the order, to apply changes to elements within the first switch fabric to achieve the state of the second switch fabric.