AI Network Upgrade Validation Using Baseline Monitoring
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Solution Overview
Problem
Conventional network upgrade processes in data centers are time-consuming and prone to errors due to manual baseline establishment and validation, lacking an automated solution for continuous monitoring and validation.
Innovation Solution
An AI-assisted system that learns normal network parameters over time to establish a baseline, automatically validates network upgrades, and reports deviations, utilizing an LLM for scalable and efficient management.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If manual baseline establishment and validation is used for network upgrades, then the process can be performed with simple tools, but it is time-consuming and prone to errors
Solution Approach 1:
The system performs self-validation by automatically comparing post-upgrade network behavior against the established baseline, eliminating the need for manual validation efforts and reducing both time and human error
Solution Approach 2:
The system continuously monitors network parameters and provides feedback by comparing actual performance against the baseline, automatically detecting deviations and alerting administrators to validation results
2Productivity
If automated monitoring system is implemented, then validation efficiency is improved, but system complexity increases
Solution Approach 1:
The AI model serves multiple functions including baseline establishment, continuous monitoring, anomaly detection, and validation automation, consolidating what would otherwise require multiple separate systems into a single unified platform
Solution Approach 2:
The AI model acts as an intermediary layer between raw network data and administrative decisions, automatically processing and interpreting network parameters to provide meaningful validation insights without requiring complex manual analysis systems
3Measurement precision
If continuous monitoring is performed to establish accurate baseline, then measurement precision is improved, but energy consumption increases
Solution Approach 1:
The system performs periodic monitoring at scheduled intervals rather than continuous monitoring, allowing the network to operate with minimal overhead between measurement cycles while still establishing accurate baselines through repeated sampling
Solution Approach 2:
The system establishes the baseline during an initial learning period before full monitoring begins, allowing the majority of subsequent operations to run with reduced monitoring intensity while maintaining validation capability
Data Source
AI summary
Embodiments of the present disclosure disclose a system that is configured to learn the normal operating parameters of the network devices in a network infrastructure over an extended period of time, thereby establishing the “baseline” for the network to provide the intended benefits.According to an embodiment of the present disclosure, a baseline variable is updated at regular intervals to make sure it reflects the actual network requirements for the applications using it. After a network upgrade, which can either be new hardware or software change, the system utilizes the baseline to automatically determine if important challenges are addressed.