Telemetry Variability Framework for ML Estimator Confidence
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Solution Overview
Problem
Computing devices providing computer-implemented services face limitations due to limited computational resources, leading to potential negative impacts such as phantom slowdowns and inaccurate predictions, which are further exacerbated by changes in resource availability and usage patterns.
Innovation Solution
A deployment manager system that generates predictions using a prediction model based on telemetry data, estimates prediction errors, and calculates confidence levels to remediate inaccuracies, thereby taking informed actions to mitigate negative impacts on service quality.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If a prediction model is used to forecast deployment performance, then service quality can be maintained, but prediction inaccuracies may lead to incorrect management decisions
Solution Approach 1:
The system implements feedback by continuously monitoring actual deployment performance and comparing it with predictions. When discrepancies are detected, the system adjusts management decisions based on actual outcomes, improving prediction accuracy over time through iterative learning and adaptation.
Solution Approach 2:
The system performs preliminary actions by generating predictions before deployment changes are implemented. This allows management to anticipate potential issues and take preventive measures, while the confidence estimation system prepares remediation strategies in advance to handle prediction inaccuracies.
2Measurement precision
If computational resources are increased to improve prediction accuracy, then prediction reliability improves, but resource availability decreases
Solution Approach 1:
The system applies partial action by using confidence estimations to determine the level of intervention needed. Instead of always using full computational resources for high-accuracy predictions, the system adjusts the degree of analysis based on the predicted impact and confidence levels, consuming resources proportionally to the situation's criticality.
Solution Approach 2:
The system changes parameters by dynamically adjusting prediction model complexity and resource allocation based on deployment criticality, historical accuracy, and current resource availability. This allows the system to optimize between prediction accuracy and resource consumption by varying model parameters rather than using fixed resource levels.
3Measurement precision
If confidence estimation is performed for all predictions, then decision accuracy improves, but processing time increases
Solution Approach 1:
The system applies local quality by performing confidence estimations selectively rather than uniformly across all predictions. Confidence analysis is concentrated on predictions that have high impact potential or low confidence levels, while routine predictions with high confidence skip detailed analysis, optimizing the balance between accuracy and processing time.
4Reliability
If prediction error estimation is used to guide management decisions, then service quality is maintained, but system complexity increases
Solution Approach 1:
The system introduces an intermediary layer that translates complex prediction error estimates into simple confidence scores and actionable recommendations. This intermediary processing layer manages the complexity by aggregating multiple error sources into unified metrics that are easier to interpret and act upon, maintaining service quality without exposing the full system complexity to decision-makers.
Data Source
AI summary
A deployment manager includes storage for storing a prediction model based on telemetry data from the deployments and a prediction manager. The prediction manager generates, using the prediction model and second telemetry data obtained from a deployment of the deployments: a prediction, and a prediction error estimate; in response to a determination that the prediction indicates a negative impact on the deployment: generates a confidence estimation for the prediction based on a variability of the second telemetry data from the telemetry data; in response to a second determination that the confidence estimation indicates that the prediction error estimate is inaccurate: remediates the prediction based on the variability to obtain an updated prediction; and performs an action set, based on the updated prediction, to reduce an impact of the negative impact on the deployment.