Telemetry Extrapolation for Real-Time Infrastructure Anomaly Prediction
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Solution Overview
Problem
The delay in telemetry data collection from infrastructure components hampers real-time incident prediction and response, leading to prolonged downtime, increased operational and reputational risks, and reduced effectiveness of automated systems.
Innovation Solution
A system that analyzes influential factors of infrastructure devices, builds a forecaster model to generate missing telemetry data in real-time, and uses machine learning to predict anomalies, incorporating techniques like weighted mean and difference calculations to extrapolate current data from near real-time data.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If telemetry data is collected and transmitted through multiple processing steps (aggregation, transmission, logging), then data is made available to the incident prediction engine, but significant latency is introduced resulting in delayed incident detection
Solution Approach 1:
The patent applies preliminary action by pre-processing and staging telemetry data in real-time buffers before it reaches the incident prediction engine. Data is collected, aggregated, and prepared in advance so that when the prediction engine needs it, ready-to-use data is immediately available, eliminating the 1-2 hour delay caused by sequential processing steps.
Solution Approach 2:
The patent introduces an intermediary data staging layer between the telemetry collection system and the incident prediction engine. This intermediary buffer holds pre-processed data and makes it immediately accessible to the prediction engine, decoupling the data collection process from the analysis process and eliminating waiting time.
2Measurement precision
If real-time telemetry data is made available to the incident prediction engine, then accurate real-time incident prediction can be achieved, but complex data processing infrastructure is required
Solution Approach 1:
The patent segments the data processing system into distinct modular components: telemetry collection agents, real-time data buffers, preprocessing modules, and the incident prediction engine. Each component performs a specific function independently, making the overall complex system manageable and maintainable while enabling real-time accurate anomaly detection.
3Reliability
If historical telemetry data is used for incident prediction, then patterns can be identified, but the data becomes outdated and less effective for real-time prediction
Solution Approach 1:
The patent implements dynamic data buffering where telemetry data is continuously updated in real-time buffers rather than being stored statically. The incident prediction engine accesses the most recent data points from these dynamic buffers, ensuring that historical patterns are identified using fresh, relevant data rather than outdated information, thus maintaining both prediction accuracy and data freshness.
Data Source
AI summary
Systems and methods are disclosed for real-time anomaly prediction using near real-time data. The invention addresses delays in telemetry data collection from infrastructure components, by collecting metrics and logging this data in real-time. Extracted logged data undergoes initial analysis to identify patterns and anomalies, followed by cleaning to remove noise and errors. Feature engineering enhances the data, creating or modifying features to improve machine learning model performance. The system calculates weighted means of previous data values and computes first and second-order differences to capture immediate changes and trends. These calculations adjust the extrapolated value to accurately reflect current conditions. The adjusted data is integrated into the dataset and validated. The validated data trains and tests a machine learning model, which is then finalized and deployed for real-time anomaly detection. This system ensures accurate and timely anomaly prediction, enabling automated incident response to maintain the reliability and performance of infrastructure components.