Adaptive Network Monitoring for Baseline Deviation Diagnosis
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Solution Overview
Problem
Network management and troubleshooting are complex and inefficient, requiring extensive manual effort and expertise, leading to prolonged Mean Time to Identify (MTTI) and Mean Time to Repair (MTTR), especially in complex networks with fragmented subject matter expertise.
Innovation Solution
Implement network intent (NI) automation, which includes adaptive monitoring and flash probes to automatically diagnose deviations from baseline configurations, provide real-time alerts, and iteratively update network intent, enabling proactive and efficient network management.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If manual network troubleshooting methods are used, then network engineers can diagnose and resolve issues, but the process is time-consuming and requires extensive expertise
Solution Approach 1:
The system performs preliminary actions by continuously monitoring network devices and comparing their configurations against baseline configurations before problems occur. This proactive approach allows the system to detect deviations early and alert operators before they become critical failures, thereby reducing MTTI and MTTR without sacrificing troubleshooting effectiveness.
Solution Approach 2:
The system implements feedback mechanisms by continuously collecting network device data, comparing it with baseline configurations, and providing alerts when deviations are detected. This closed-loop feedback enables rapid response to configuration changes and potential issues, significantly reducing the time to identify and repair problems while maintaining reliable diagnostics.
2Reliability
If conventional network monitoring is used, then basic network status can be tracked, but adaptive monitoring to capture network problems in advance is not achieved
Solution Approach 1:
The system applies dynamics by making the monitoring adaptive rather than static. It dynamically adjusts monitoring based on baseline configurations and automatically detects deviations from expected behavior. This dynamic approach enables the system to adapt to different network conditions and detect problems proactively, enhancing both reliability and adaptability simultaneously.
Solution Approach 2:
The system performs preliminary actions by establishing baseline configurations and continuously comparing current states against these baselines before problems manifest. This proactive monitoring capability allows the system to detect potential issues early, improving reliability while demonstrating advanced adaptability through automated deviation detection.
3Ease of operation
If extensive training is provided to junior network engineers, then troubleshooting knowledge is transferred, but the complexity and difficulty of sharing troubleshooting methodology remains
Solution Approach 1:
The system applies copying by automatically capturing and replicating troubleshooting knowledge and baseline configurations across the network. Instead of manually training engineers on complex methodologies, the system copies proven configurations and diagnostic approaches, making expert knowledge readily available and significantly easing the transfer of troubleshooting skills while reducing perceived complexity.
Solution Approach 2:
The system implements self-service by automatically performing configuration comparisons, deviation detection, and diagnostic functions that would otherwise require extensive engineer expertise. This automation reduces the complexity barrier for junior engineers while maintaining high troubleshooting effectiveness, as the system serves itself by continuously monitoring and self-diagnosing network issues.
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.