Telemetry Gap Forecasting for Real-Time 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, and generates missing telemetry data in real-time using machine learning and statistical models to ensure the incident prediction engine operates with current data, incorporating techniques like weighted mean and difference calculations to extrapolate current states.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If telemetry data is collected from infrastructure components, then the incident prediction engine can detect anomalies, but significant delay (1-2 hours) occurs in data collection and transmission
Solution Approach 1:
The patent applies preliminary action by collecting and storing telemetry data in advance before incidents occur. The system continuously gathers infrastructure data (CPU usage, memory consumption, network traffic) and stores it in a database, enabling the prediction engine to analyze historical patterns and forecast potential failures before they actually happen, thus resolving the contradiction between having sufficient data for accurate detection and the time delay in data collection.
2Measurement precision
If real-time data collection is implemented, then incident prediction accuracy improves, but system complexity and data processing requirements increase
Solution Approach 1:
The patent applies local quality by selectively collecting and processing only the most critical telemetry data parameters relevant to infrastructure health (CPU usage, memory consumption, network traffic, disk space). Instead of attempting to collect all possible data uniformly across the entire infrastructure, the system identifies and focuses on key metrics that most strongly correlate with incident risk, thereby reducing data processing complexity while maintaining high measurement precision for incident prediction.
3Productivity
If delayed telemetry data is used, then data transmission and processing time is reduced, but the ability to respond to incidents in real-time is compromised
Solution Approach 1:
The patent applies preliminary action by pre-collecting and storing telemetry data in a database before incidents occur. This allows the system to process data efficiently in batches rather than requiring real-time continuous processing, while still enabling timely incident detection through predictive analysis of historical patterns. The prediction engine can quickly query pre-stored data to identify anomalies without the burden of real-time data transmission and processing.
4Difficulty of detecting and measuring
If comprehensive telemetry data collection is implemented, then root cause analysis improves, but network bandwidth and data transmission load increase
Solution Approach 1:
The patent applies local quality by selectively collecting only the most relevant telemetry parameters (CPU usage, memory consumption, network traffic, disk space) rather than attempting to capture all possible infrastructure data. This targeted approach provides sufficient information for effective root cause analysis while minimizing network bandwidth consumption and data transmission load. The system focuses data collection on metrics that have the highest correlation with infrastructure failures, thereby reducing the quantity of data transmitted without compromising analytical capability.
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 like CPU usage, memory consumption, network traffic, and error rates, 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 cleaned and engineered data is ingested into a data warehouse for storage. Gaps in time series data are filled using influencer factor analysis and forecasting. The complete dataset 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.