Neural Network Graph for Dynamic Data Center Asset Health Scoring
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Solution Overview
Problem
Current data center asset health scoring methods are inadequate as they do not account for the number of issues, severity, ease of resolution, or dynamic factors, and fail to provide a comprehensive assessment of asset health, nor do they allow for user input or consideration of the operating environment, leading to incomplete and ineffective health score calculations.
Innovation Solution
A method and system that utilize a neural network graph to calculate data center asset health scores by analyzing similarity between assets, incorporating user feedback, and weighting edge connections based on issue similarity and resolution complexity, enabling a more nuanced and dynamic assessment of asset health.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional health scoring methods are used, then the calculation process is simple, but the assessment accuracy is insufficient as it does not account for multiple factors including number of issues, severity, ease of resolution, and dynamic factors
Solution Approach 1:
The health scoring system is segmented into multiple independent components: issue detection module, severity assessment module, resolution complexity evaluation module, and neural network processing module. Each module handles a specific aspect of health assessment independently, allowing the system to achieve high accuracy through comprehensive factor consideration while maintaining manageable complexity through modular architecture.
Solution Approach 2:
A neural network graph is introduced as an intermediary between the raw health information and the final health score. The neural network processes multiple input factors (number of issues, severity, ease of resolution, dynamic factors) and transforms them into a comprehensive health assessment, enabling accurate scoring without requiring complex direct calculation formulas.
2Reliability
If comprehensive health assessment factors are incorporated, then the health score becomes more accurate, but the system complexity increases due to multiple evaluation dimensions
Solution Approach 1:
The neural network graph serves multiple functions simultaneously: it processes issue data, evaluates severity, assesses resolution complexity, and incorporates dynamic factors. This multi-functional approach allows the system to achieve reliable comprehensive health assessment without proportionally increasing system complexity, as a single neural network structure handles multiple evaluation dimensions.
Solution Approach 2:
The system dynamically adjusts evaluation parameters based on the specific characteristics of each asset and issue. The neural network learns to weight different factors (number of issues, severity, ease of resolution) differently for each asset type and situation, enabling reliable assessment across diverse data center environments without requiring rigid complex rules for each scenario.
3Adaptability or versatility
If dynamic factors and user feedback are considered, then the health score becomes more meaningful, but the data processing complexity increases
Solution Approach 1:
The system incorporates user feedback and operational context as input parameters to the neural network. This feedback mechanism allows the health score to adapt to real-world conditions and user perceptions, making the assessment more meaningful and actionable. The neural network processes this feedback information alongside other data factors, managing the increased processing complexity through its learned patterns and relationships.
Solution Approach 2:
The health scoring system is designed to be dynamic, continuously updating asset health scores based on changing conditions, new issues, and user feedback. The neural network adapts its processing to accommodate varying data characteristics and temporal patterns, enabling the system to maintain high adaptability while managing data processing complexity through efficient neural computation rather than rigid complex algorithms.
Data Source
AI summary
A system, method, and computer-readable medium are disclosed for performing a data center asset management and monitoring operation. The data center asset management and monitoring operation includes: receiving data center asset health information from respective data center assets from a plurality of data center assets; generating a neural network graph using the data center asset health information from the plurality of respective data center assets, the neural network graph comprising a plurality of nodes; calculating node edge weights based upon how similar certain data center assets are to other data center assets; and, calculating a data center asset health score using the neural network graph.