Proactive Thermal Remediation via Historical Data Prediction
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Solution Overview
Problem
Mobile computing devices face performance limitations due to heat dissipation issues, leading to reduced responsiveness and lower frame rates, as thermal remediations are often applied after the device reaches a thermal threshold, resulting in worse performance compared to proactive measures.
Innovation Solution
A computer system captures and stores historical thermal remediation data, predicts future remediation needs based on current and prior operating states, and communicates with applications to implement sustainable thermal remediations, such as adjusting CPU frequencies, display brightness, and hardware idle states, to optimize performance and extend device life.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Temperature
If thermal remediations are applied after reaching thermal threshold, then device temperature is reduced, but processing performance deteriorates due to reduced responsiveness and lower frame rates
Solution Approach 1:
The system performs preliminary thermal remediation actions by predicting future thermal states based on historical data and current operating conditions. Thermal remediations are applied proactively before the device reaches thermal thresholds, preventing performance degradation while maintaining effective temperature control. The system analyzes patterns from historical thermal data to anticipate when thermal intervention will be needed and prepares remediation actions in advance.
2Temperature
If thermal remediations are applied continuously, then device temperature remains controlled, but battery life is reduced due to sustained remediation intensity
Solution Approach 1:
The system applies partial thermal remediation actions by determining the minimum necessary remediation intensity based on predicted thermal futures. Instead of applying maximum remediation continuously, the system adjusts remediation levels to match the actual thermal risk, using historical data to identify the least intensive effective remediation. This partial action approach maintains temperature control while minimizing battery consumption.
Solution Approach 2:
The system dynamically changes remediation parameters based on predicted thermal conditions and historical patterns. By analyzing historical thermal data and current operating state, the system adjusts remediation intensity, timing, and type to optimize the balance between temperature control and battery life. Parameters such as CPU frequency scaling, display brightness, and haptic feedback intensity are modulated according to predicted thermal needs rather than applied at fixed levels.
3Measurement precision
If historical thermal remediation data is collected and analyzed, then prediction accuracy improves, but system complexity increases due to data storage and processing requirements
Solution Approach 1:
The system extracts only the most relevant thermal features from historical data for prediction purposes. Instead of storing and processing complete historical thermal logs, the system identifies and extracts key predictive features such as thermal patterns, workload characteristics, and remediation effectiveness metrics. This extraction approach maintains high prediction accuracy while minimizing data storage requirements and processing complexity.
Data Source
AI summary
Embodiments are directed to capturing and storing historical data regarding thermal remediations, to predicting and acting on remediation futures and to communicating with applications regarding thermal remediations implemented on the computer system. In one scenario, a computer system determines which thermal remediations are currently being implemented on a monitored computing device. The thermal remediations are based on the monitored computing device's current operating environment including the physical thermal environment and/or the current software execution environment. The computer system further tracks thermal remediation levels for those thermal remediations that are currently being implemented on the monitored computing device, the thermal remediation levels indicating the degree to which each thermal remediation is implemented. The computer system also filters the tracked thermal remediation levels for tracked thermal remediation information that includes information regarding the computing device's current operating environment, and stores the filtered thermal remediation levels in a data store.