Network Assessment Automation via Global Intent Data Table
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Solution Overview
Problem
Conventional network troubleshooting methods are inefficient and require extensive manual effort, making it difficult for junior engineers to diagnose and resolve network issues, especially in complex modern networks, where repetitive problems often arise from misconfiguration or performance degradation, and lack of automated enforcement of design rules and security policies.
Innovation Solution
The implementation of a Problem Diagnosis Automation System (PDAS) using an Automation Data Table (ADT) that automates network assessment, monitoring, and troubleshooting by creating a global data table for managing network assets and intents, enabling automated diagnosis and enforcement of preventive measures across the network.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If conventional manual troubleshooting methods are used, then network engineers can diagnose and resolve network issues, but the process requires extensive time and manual effort, making it inefficient especially for junior engineers
Solution Approach 1:
The system performs preliminary actions by automatically collecting network device data, configurations, and performance metrics continuously before problems occur. This pre-collected data is stored and structured in advance, so when a troubleshooting event occurs, the system can immediately query and analyze relevant information without manual data gathering, significantly reducing diagnosis time and improving troubleshooting efficiency
Solution Approach 2:
The patent introduces an intermediary automation system that acts as a mediator between network engineers and network devices. This intermediary automatically executes troubleshooting commands, collects device responses, analyzes data patterns, and presents synthesized diagnostic results to engineers. This intermediary layer handles the time-consuming manual operations, allowing engineers to focus on decision-making while the system manages data collection and initial analysis
2Ease of operation
If extensive training is provided to junior network engineers, then they can learn troubleshooting commands and processes, but the learning curve remains steep and knowledge transfer is difficult
Solution Approach 1:
The system implements self-service by providing automated guidance and diagnostic recommendations that empower junior engineers to perform complex troubleshooting tasks independently. The automation system analyzes network data, identifies potential issues, and suggests remediation steps without requiring engineers to have deep expertise in every network protocol or device command, thereby reducing the need for extensive training while maintaining effective troubleshooting capability
Solution Approach 2:
The patent replaces the mechanical system of human learning and memory with an automated intelligent system. Instead of requiring engineers to memorize numerous troubleshooting commands and procedures through years of training, the system uses automated data collection, analysis algorithms, and knowledge bases to perform the complex diagnostic work, substituting human cognitive effort with automated processing that requires minimal training
3Reliability
If manual troubleshooting commands and processes are executed for each device, then network problems can be diagnosed, but the complicated methodology is hard to share and transfer across similar network problems
Solution Approach 1:
The system implements universality by creating a standardized automated troubleshooting framework that can be applied across multiple network devices, protocols, and problem types. The automation platform uses universal data collection mechanisms, standardized analysis templates, and reusable diagnostic playbooks that work consistently across different network scenarios. This universal approach ensures reliable and accurate diagnosis while eliminating the need for complex, device-specific manual procedures, as the same automated system handles diverse troubleshooting situations uniformly
Solution Approach 2:
The patent applies parameter changes by dynamically adjusting the depth and scope of automated analysis based on the specific problem type, network configuration, and available data. The system modifies troubleshooting parameters such as data collection intensity, analysis algorithms applied, and diagnostic thoroughness automatically according to the situation, rather than using a fixed complex methodology for all cases. This adaptive parameter adjustment maintains high diagnostic reliability while reducing unnecessary complexity in each individual troubleshooting instance
4Loss of time
If automated network assessment and monitoring are implemented, then Mean Time to Repair (MTTR) is reduced and network management efficiency is improved, but the system complexity increases
Solution Approach 1:
The system applies segmentation by dividing the automated network assessment and monitoring functionality into distinct modular components: data collection modules for different device types, analysis modules for specific problem categories, and reporting modules for various stakeholder needs. Each module operates independently but integrates through standardized interfaces. This segmentation reduces system complexity by making each component manageable and replaceable, while the coordinated operation of these modules achieves rapid automated diagnosis and significantly reduced MTTR
Data Source
AI summary
Network management automation may use network intent (NI) which represents a network design and baseline configuration for that network or network devices with the ability to diagnose deviation from the baseline configuration. This may include automation of the diagnosis of repetitive problems and the enforcement of preventive measures across a network. This automation may be through an automation data table (ADT), which is an extended global data table for managing network assets and network intents associated with those network assets. ADT is a database for intents, supporting intent creation and replication, and can be used in the PDAS system.