Peak-Aware Response Time Prediction via Telemetry Segmentation
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Solution Overview
Problem
Existing systems face challenges in predicting and ensuring responsiveness of deployment configurations, particularly in identifying and mitigating peak response times, which affects the reliability of computer implemented services.
Innovation Solution
A prediction manager system that utilizes persistent storage and multiple prediction frameworks to generate peak-aware predictions by obtaining training data, performing peak extraction analysis, and combining response time predictions with confidence indicators to determine reliable deployment configurations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional prediction models are used to estimate response times, then the prediction process is simple, but the accuracy is insufficient especially during peak periods
Solution Approach 1:
The patent divides the prediction task into two separate models: a response time prediction model and a confidence prediction model. This segmentation allows each model to specialize in one aspect, improving overall accuracy while maintaining manageable complexity through modular architecture
Solution Approach 2:
The patent introduces confidence predictions as an intermediary element that bridges the gap between raw response time measurements and reliable predictions. This intermediary layer filters and validates predictions, significantly improving accuracy during peak periods without requiring complete system redesign
2Reliability
If multiple prediction frameworks are combined to improve accuracy, then the prediction reliability improves, but the computational complexity increases
Solution Approach 1:
The patent merges two prediction frameworks (response time model and confidence model) into a unified prediction pipeline. The combination is achieved through integrated training data structures and coordinated prediction logic, improving reliability while controlling complexity through shared infrastructure
Solution Approach 2:
The patent creates a multi-functional prediction system where the same infrastructure supports both response time estimation and confidence assessment. This universal architecture reduces overall complexity compared to maintaining separate independent systems, as both functions share data processing pipelines and model training mechanisms
3Reliability
If peak response times are excluded from training data, then the confidence of predictions improves, but the quantity of training data decreases
Solution Approach 1:
The patent extracts peak response times from the training dataset and handles them through a specialized confidence prediction model. This extraction improves overall prediction confidence by preventing peak anomalies from corrupting general predictions, while the separated handling ensures peak data is still utilized effectively without compromising data quantity
Data Source
AI summary
A prediction manager for providing responsiveness predictions for deployments includes persistent storage and a predictor. The persistent storage stores training data and conditioned training data. The predictor is programmed to obtain training data based on: a configuration of at least one deployment of the deployments, and a measured responsiveness of the at least one deployment, perform a peak extraction analysis on the measured responsiveness to obtain conditioned training data, obtain a prediction model using: the training data, and a first untrained prediction model, obtain a confidence prediction model using: the conditioned training data, and a second untrained prediction model, obtain a combined prediction using: the prediction model, and the confidence prediction model, and perform, based on the combined prediction, an action set to prevent a responsiveness failure.