Automation Data Table for Network Intent Diagnosis
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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, performance degradation, or security violations, lacking automated methods to enforce design rules and best practices.
Innovation Solution
The implementation of a Problem Diagnosis Automation System (PDAS) using an Automation Data Table (ADT) that automates the diagnosis of repetitive problems and enforces preventive measures across the network, leveraging Network Intent (NI) to manage and replicate network assets and intents, facilitating automated troubleshooting and maintenance.
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 it requires extensive time and manual effort, making it inefficient especially for junior engineers
Solution Approach 1:
The system performs preliminary actions by automatically executing standardized troubleshooting commands and procedures before human intervention is needed. The automated data table pre-configures diagnostic workflows, so when an issue occurs, the system immediately begins executing predetermined troubleshooting steps, eliminating the need for engineers to start from scratch and reducing MTTR.
Solution Approach 2:
The system enables self-service by allowing the automated troubleshooting framework to diagnose and resolve network issues independently without requiring extensive human intervention. The data table contains self-contained diagnostic logic that can identify problems and execute repairs autonomously, freeing engineers from repetitive manual tasks and improving overall productivity.
2Adaptability or versatility
If standardized troubleshooting commands are used, then troubleshooting processes can be replicated, but the complicated methodology is hard to share and transfer across different instances
Solution Approach 1:
The automated data table serves as a universal repository that stores troubleshooting knowledge applicable across multiple network devices and scenarios. Instead of maintaining separate procedures for each device, the system uses a single standardized framework that can be applied universally, making troubleshooting methodology easily shareable and transferable across different instances while reducing complexity through standardization.
Solution Approach 2:
The system uses copying by replicating proven troubleshooting procedures from the automated data table across multiple network instances. Once a diagnostic workflow is validated in one instance, it can be copied and applied to similar situations elsewhere, enabling rapid knowledge transfer and consistent troubleshooting approaches without requiring engineers to recreate procedures from scratch.
3Loss of information
If manual troubleshooting procedures are documented, then knowledge can be captured and shared, but the information may become outdated or inaccurate without continuous updates
Solution Approach 1:
The automated system incorporates feedback mechanisms that continuously monitor network device responses and outcomes from executed troubleshooting procedures. This feedback loop allows the system to learn from actual performance data and automatically update the automated data table with refined diagnostic logic, ensuring troubleshooting knowledge remains current and accurate without requiring manual documentation updates.
Solution Approach 2:
The troubleshooting knowledge base transitions from static documentation to a dynamic, self-updating system. The automated data table continuously adapts based on real-world performance data and emerging network issues, allowing the troubleshooting procedures to evolve automatically rather than requiring periodic manual revisions, thereby maintaining accuracy while reducing maintenance effort.
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.