Remote System Shutdown Prediction With Multi-Horizon Error Models
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Solution Overview
Problem
Data collection systems fail to predict and inefficiently handle remote system data outages, leading to resource wastage and missed data opportunities due to unawareness of outage periods and reliance on arbitrary suspension times.
Innovation Solution
Employing a system that uses multiple regression models to predict error rates over different time intervals and applies policy rules to determine error modes, optimizing data collection by pausing or resuming attempts based on predicted error rates.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of energy
If data collection systems use arbitrary suspension time periods to handle remote system outages, then the system can pause data collection attempts, but it misses data opportunities and wastes resources because the suspension time is inaccurate
Solution Approach 1:
The system performs preliminary actions by training multiple regression models on historical error data before actual data collection occurs. These models predict error rates for different time intervals in advance, allowing the system to proactively identify outage periods and adjust data collection strategies before resources are wasted or data opportunities are missed.
Solution Approach 2:
The system changes parameters by using multiple regression models that predict error rates for different time intervals (15min, 30min, 60min, 24h) rather than relying on a single arbitrary suspension time. The policy engine dynamically adjusts the suspension duration based on the predicted error rates and selected time interval, optimizing the balance between avoiding wasted resources and capturing available data.
2Productivity
If data collection systems continue to attempt data collection from unavailable remote systems, then the system maintains continuous data collection activity, but it wastes compute, network activity, memory allocation, and deployment costs
Solution Approach 1:
The system implements feedback by continuously monitoring actual error data from remote systems and using this information to train and refine multiple regression models. The policy engine receives predicted error rates as feedback and dynamically adjusts data collection strategies, creating a closed-loop system that learns from past performance and optimizes future resource allocation based on real-world outcomes.
Solution Approach 2:
The system performs preliminary error rate predictions using trained regression models before initiating data collection attempts. This allows the system to anticipate potential outages and suspend data collection in advance, preventing wasted compute, network, and memory resources while maintaining high productivity during available periods.
3Measurement precision
If data collection systems use a single regression model to predict error rates, then the system simplifies the prediction process, but it cannot accurately predict error rates across different time intervals leading to suboptimal suspension decisions
Solution Approach 1:
The system applies segmentation by dividing the error rate prediction task into multiple specialized regression models, each trained to predict error rates for a specific time interval (15min, 30min, 60min, 24h). This segmentation allows each model to focus on capturing patterns specific to its time horizon, improving overall prediction accuracy while maintaining manageable complexity through modular architecture.
Solution Approach 2:
The system achieves universality by creating a multi-functional prediction framework where multiple regression models work together to provide comprehensive error rate predictions across different time intervals. The policy engine universally applies these predictions to make suspension decisions regardless of the selected time interval, making the system adaptable to various data collection requirements and outage scenarios.
Data Source
AI summary
Certain aspects of the disclosure provide a method for detecting data collection errors by processing error data with a plurality of regression models to generate a plurality of predicted error rates over a plurality of time intervals. The method includes determining an error mode by applying a set of policy rules optimized for determining the error mode to the plurality of predicted error rates.