Kalman Filter Grid Telemetry Error Detection
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Solution Overview
Problem
In grid computing engine topologies, managing and controlling large amounts of telemetry data becomes costly and inefficient as systems grow, leading to unpredictable system management, false alarms, and inadequate filtering of telemetry data, necessitating real-time detection of error states without extensive data storage.
Innovation Solution
Implementing Kalman filters at nodes within the grid computing engine to monitor telemetry data, determine if it's outside predictive bounds, signal error conditions, and self-correct issues, thereby reducing the need for large data storage and improving real-time error detection and management.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If large telemetry datasets are stored for grid engine management, then more comprehensive monitoring and analysis is possible, but storage cost and time consumption increase significantly
Solution Approach 1:
The patent extracts only the essential information from telemetry data by using Kalman filters to compute predicted values and compare them with actual measurements. Instead of storing raw telemetry datasets, the system stores and processes only the filtered and predicted values, which contain the critical error detection information while eliminating redundant data.
Solution Approach 2:
The Kalman filter performs preliminary processing of telemetry data in real-time by continuously computing predicted values based on system models and comparing them with actual measurements. This preliminary action identifies potential errors before they manifest as significant problems, allowing the system to store and analyze only the processed error indicators rather than raw data.
2Quantity of substance
If threshold-based monitoring is used to filter telemetry data, then most telemetry data can be filtered out, but false alarms and inability to adapt to selective data occur
Solution Approach 1:
The Kalman filter implements feedback by continuously comparing predicted telemetry values with actual measurements and adjusting the model parameters accordingly. This feedback mechanism allows the system to adapt to changing system conditions and selectively filter data based on actual deviations from predicted behavior, rather than using fixed thresholds that cause false alarms.
Solution Approach 2:
The system dynamically changes the filtering parameters by adjusting the Kalman filter coefficients based on the specific telemetry data characteristics and system state. This allows the filter to adapt to selective data patterns and change its sensitivity thresholds dynamically, eliminating false alarms while maintaining accurate error detection.
3Productivity
If grid computing systems handle more transactions, then system capability increases, but management and troubleshooting become more complex and costly
Solution Approach 1:
The patent extracts and focuses only on the critical error detection functions from the complex telemetry data stream. By using Kalman filters to identify and isolate actual errors from the noise of normal operational variations, the system reduces management complexity while maintaining the ability to handle high transaction volumes.
Solution Approach 2:
The Kalman filter system provides self-service error detection by automatically comparing predicted versus actual values and generating error signals without requiring complex manual analysis. This automated self-diagnosis capability allows the system to manage itself as it handles increasing transaction loads, reducing the need for manual troubleshooting.
Data Source
AI summary
In one embodiment, a method and apparatus for Kalman filtering for grid computing telemetry and workload management is disclosed. The method includes monitoring telemetry data at a node of a grid computing engine with a Kalman filter, determining whether the monitored telemetry data is outside of a bounds of a predictive model of the Kalman filter, signaling an error condition for the node if the monitored telemetry data is outside of the bounds, and addressing the error condition by correcting a problem experienced at the node corresponding to the error condition. Other embodiments are also disclosed.