Dynamic Shutdown Interval Tuning via Machine Learning
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Solution Overview
Problem
The shutdown of modern computing systems is slow due to the use of predefined static intervals between shutdown commands for each subsystem, leading to unnecessary delays and potential premature force shutdowns.
Innovation Solution
Implementing a computer-implemented method that uses a trained machine learning model to determine custom shutdown intervals for each subsystem, allowing for dynamic adjustment based on observed shutdown data and operational characteristics.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If predefined static intervals are used between shutdown commands for each subsystem, then the shutdown process is simple and reliable, but the shutdown time is substantial and slow
Solution Approach 1:
The patent applies dynamics by replacing static predefined intervals with dynamic adaptive intervals. The system learns optimal wait times between shutdown commands through machine learning models that adapt to actual subsystem behavior, allowing the shutdown process to adjust timing based on real-time conditions rather than fixed schedules.
Solution Approach 2:
The patent changes the parameter of shutdown intervals from fixed static values to dynamic learned values. By using machine learning models to predict optimal wait times based on historical data and system state, the parameters governing shutdown timing are transformed from constants to adaptive variables that optimize shutdown speed while maintaining reliability.
2Reliability
If longer intervals are used between shutdown commands to ensure subsystem shutdown, then shutdown reliability is maintained, but shutdown speed decreases and unnecessary delays occur
Solution Approach 1:
The patent implements feedback mechanisms where the system monitors whether subsystems actually shut down within expected timeframes and uses this information to adjust future interval predictions. The machine learning model learns from outcomes of previous shutdown attempts, refining its predictions to achieve optimal balance between reliability and speed.
Solution Approach 2:
The system performs preliminary learning during normal operation by observing subsystem behavior patterns and building predictive models before actual shutdown events. This preliminary action allows the system to have optimized interval predictions ready in advance, eliminating the need for conservative long waits during actual shutdown while maintaining reliability.
3Productivity
If shorter intervals are used between shutdown commands to reduce shutdown time, then shutdown speed improves, but premature force shutdowns may occur
Solution Approach 1:
The patent uses dynamics to adjust intervals based on learned subsystem characteristics. Rather than using fixed short intervals that risk premature termination, the system dynamically determines optimal wait times based on actual subsystem behavior patterns, allowing sufficiently long intervals when needed and shorter intervals when safe, thus preventing force shutdowns while maintaining speed.
Solution Approach 2:
The system changes the shutdown interval parameter from a fixed conservative value to a dynamic learned value that adapts to each subsystem's actual shutdown characteristics. This parameter transformation allows the system to use the minimum necessary wait time for each specific subsystem, preventing premature force shutdowns while optimizing overall shutdown speed.
4Reliability
If sequential shutdown of multiple subsystems is performed with static intervals, then data consistency is ensured, but total shutdown time becomes substantial
Solution Approach 1:
The patent transforms the shutdown interval parameters from uniform static values to subsystem-specific dynamic values. Each subsystem receives customized intervals based on its individual characteristics learned by the machine learning model, allowing the system to minimize wait times where possible while maintaining data consistency requirements, thereby reducing total shutdown time.
Solution Approach 2:
The patent segments the shutdown process into subsystem-specific operations with individually optimized intervals. Rather than applying a single static interval to all subsystems, the system divides the shutdown process and applies learned optimal intervals to each subsystem based on its specific behavior patterns, reducing cumulative wait time while maintaining reliability.
Data Source
AI summary
Computer-implemented methods for performing shutdown of a computing system using custom shutdown intervals are provided. Aspects include receiving, by the computing system, a command to shutdown the computing system and issuing a first command to shutdown a first subsystem of the computing system. Aspects also include determining, after a first interval since issuing the first command, that the first subsystem has not shutdown and issuing a second command to shutdown the first subsystem of the computing system, wherein the first interval is obtained from a trained machine learning model.