Dynamic Telemetry Collection for Data Processing Health Inference
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Solution Overview
Problem
In distributed systems, managing data processing systems is challenging due to limited computing resources, where existing methods fail to efficiently collect telemetry data to infer health states and reduce the likelihood of system impairment without excessive computational overhead.
Innovation Solution
A method that dynamically updates telemetry data collection rates and quantities based on confidence levels in inferred health states, increasing collection when confidence is low and reducing it when high, using anomaly detection and rule-based analysis to identify suspect quantities and adjust collection plans, and employing reinforcement learning to update inference models.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If telemetry data collection rate and quantity are increased to improve health state inference accuracy, then measurement precision is improved, but use of energy and computational overhead increase
Solution Approach 1:
The patent implements dynamic adjustment of telemetry data collection rates and quantities based on the confidence level of health state inferences. When confidence is low, the system increases collection rates and quantities to improve inference accuracy. When confidence is high, the system reduces collection rates and quantities to conserve computational resources. This dynamic adaptation resolves the contradiction by making the measurement precision and resource consumption variable rather than fixed.
Solution Approach 2:
The system changes key parameters (collection rate, collection quantity) based on the confidence level of health state inferences. By adjusting these parameters dynamically, the system optimizes the balance between measurement precision and computational overhead, collecting more data only when necessary to improve inference accuracy.
2Reliability
If telemetry data collection rate and quantity are increased to reduce likelihood of system impairment, then reliability is improved, but use of energy and computational overhead increase
Solution Approach 1:
The system dynamically adjusts telemetry data collection based on confidence levels in health state inferences. When confidence is low (indicating potential reliability issues), the system increases collection rates and quantities to better assess system health and prevent impairment. When confidence is high, the system reduces collection to conserve resources, maintaining reliability while optimizing energy usage.
Solution Approach 2:
The system uses feedback from health state inference confidence levels to control telemetry data collection. The confidence level serves as a feedback signal that triggers adjustments in collection rate and quantity, creating a closed-loop control system that balances reliability monitoring with computational resource conservation.
3Measurement precision
If telemetry data collection is performed at high rates to improve health state inference accuracy, then measurement precision is improved, but productivity is reduced due to resource consumption
Solution Approach 1:
The patent implements dynamic adjustment of telemetry data collection rates and quantities based on the confidence level of health state inferences. When confidence is low, the system increases collection rates and quantities to improve inference accuracy. When confidence is high, the system reduces collection rates and quantities to conserve computational resources. This dynamic adaptation resolves the contradiction by making the measurement precision and resource consumption variable rather than fixed.
Solution Approach 2:
The system changes key parameters (collection rate, collection quantity) based on the confidence level of health state inferences. By adjusting these parameters dynamically, the system optimizes the balance between measurement precision and computational overhead, collecting more data only when necessary to improve inference accuracy.
Data Source
AI summary
Methods and systems for managing operation of data processing systems are disclosed. To manage operation of the data processing systems, telemetry data for the data processing systems may be collected and used to estimate the health of the data processing systems. The rates and types of telemetry data this is collected may be dynamically adjusted based on the confidence in estimates for the health of the data processing systems. The collection rate and/or number of monitored quantities may be increased as the confidence in the estimated health of the data processing systems decreases.