Telemetry State Matching for Infrastructure Outage Prediction
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Solution Overview
Problem
Current systems for predicting and preventing technology infrastructure failures often fail to accurately consider historical events leading up to a current state, reducing the accuracy of failure detection and prevention.
Innovation Solution
A computing platform configures a rules-based state machine to analyze telemetry data, generating state images and identifying patterns to predict system failures by comparing initial and additional telemetry state images and their transitions, sending preemptive resolution commands when a likelihood of failure is detected.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Speed
If current system performance is analyzed in isolation, then analysis speed is improved, but failure detection accuracy deteriorates
Solution Approach 1:
The system performs preliminary actions by generating state images from historical telemetry data and pre-establishing a rules-based state machine with failure patterns before actual failure detection is needed. This allows the system to have failure patterns pre-identified and ready for comparison, maintaining high speed while improving accuracy through historical context
Solution Approach 2:
The system creates a copy of the system state in the form of telemetry state images that represent historical configurations. These copied state images are stored and compared against current states to identify failure patterns, enabling accurate detection without requiring real-time analysis of entire historical datasets
2Measurement precision
If historical telemetry data is fully analyzed, then failure detection accuracy is improved, but processing time increases
Solution Approach 1:
The system extracts only the essential failure patterns and state transitions from historical telemetry data, rather than analyzing entire historical datasets. By identifying and storing only the critical state images and transitions that lead to failures, the system maintains high detection accuracy while significantly reducing processing time for current state analysis
Solution Approach 2:
Historical analysis is performed in advance to build the rules-based state machine with pre-identified failure patterns. This preliminary processing separates the time-consuming historical analysis from real-time failure detection, allowing accurate comparisons to be made quickly against pre-established failure patterns
Data Source
AI summary
A computing platform may configure a rules-based state machine to predict system failure for a system based on telemetry state images and transitions between the telemetry state images. The computing platform may receive initial telemetry data. The computing platform may generate, based on the initial telemetry data, an initial telemetry state image. The computing platform may receive additional telemetry data, and may generate, based on the additional telemetry data, an additional telemetry state image. The computing platform may compare a pattern, corresponding to the initial telemetry state image, the additional telemetry state image, and a corresponding transition, to historical patterns to identify a match. The computing platform may identify, using the identified matching pattern, a likelihood of failure for the system, and may send, based on the likelihood of failure for the system, preemptive resolution commands causing modification of operations at the system to prevent a predicted failure.