Network Intent Monitoring for Faster Troubleshooting Diagnosis
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Solution Overview
Problem
Traditional network management and troubleshooting methods are complex, time-consuming, and inefficient, especially for junior engineers, due to the need for manual processes and lack of effective knowledge transfer, leading to prolonged Mean Time to Identify (MTTI) and Mean Time to Repair (MTTR) in network incidents.
Innovation Solution
Implementing network intent-based automation and adaptive monitoring, utilizing flash probes and dashboards to automate network management, diagnose deviations, and provide real-time alerts, enabling proactive and efficient network management.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If manual troubleshooting methods are used with standard commands and processes, then network engineers can diagnose and resolve issues, but the process becomes time-consuming and requires extensive training, increasing Mean Time to Identify (MTTI) and Mean Time to Repair (MTTR)
Solution Approach 1:
The system performs preliminary actions by automatically collecting network device data, comparing it against baseline configurations, and pre-identifying deviations before they cause outages. This proactive approach eliminates the need for manual troubleshooting steps and significantly reduces MTTI and MTTR by having diagnostics ready in advance.
Solution Approach 2:
The network management system performs self-service by automatically monitoring its own network devices, detecting configuration deviations, and generating diagnostic reports without human intervention. This automation eliminates the time-consuming manual execution of troubleshooting commands and processes.
2Loss of time
If comprehensive network monitoring and automation tools are implemented, then MTTI and MTTR are reduced, but the device complexity and initial setup requirements increase
Solution Approach 1:
The system achieves universality by creating a single automated platform that performs multiple functions: data collection, baseline comparison, deviation detection, and diagnostic report generation. This multi-functional approach reduces overall system complexity compared to implementing separate tools for each function while still achieving rapid troubleshooting.
Solution Approach 2:
The system uses copying by replicating baseline configurations from known-good network device states and comparing current states against these copies. This allows automatic detection of deviations without complex analysis algorithms, simplifying the system while maintaining effective monitoring capabilities.
3Loss of information
If traditional manual troubleshooting documentation is used, then troubleshooting knowledge can be captured, but the knowledge is poorly documented, hard to share and transfer, and requires extensive training for junior engineers
Solution Approach 1:
The system implements feedback by automatically generating diagnostic reports that capture troubleshooting knowledge and deviations, then using this information to update baseline configurations and improve future diagnostics. This closed-loop feedback mechanism ensures knowledge is continuously captured, documented, and made available for transfer to junior engineers without requiring extensive manual documentation.
Data Source
AI summary
A system is disclosed for network management automation using network intent or adaptive monitoring automation. Network intent (NI) represents a network design and baseline configuration for that network or network devices with an ability to diagnose deviation from the baseline configuration. The NI can be automated to update and replicate the diagnosis. The monitoring of the network can be adapted to capture network problems in advance with adaptive monitoring automation.