Intelligent Polling Configuration for Predicted Task Completion
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Solution Overview
Problem
Existing polling systems face challenges in optimizing polling intervals, leading to inefficiencies in resource utilization and timeliness of data retrieval due to fixed intervals that do not adapt to varying task completion times.
Innovation Solution
A machine learning model is trained to predict task execution times, allowing for dynamic polling configurations that adjust polling frequencies based on predicted completion times, including initial, increased, or decreased frequencies as the completion time approaches, and error handling to optimize resource use.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If fixed polling intervals are used, then system simplicity is maintained, but resource utilization efficiency deteriorates
Solution Approach 1:
The patent implements dynamic polling intervals that automatically adjust based on task completion patterns. The system transitions from fixed to variable polling frequencies, increasing polling rate when tasks complete quickly and decreasing it when tasks take longer, thereby optimizing resource utilization while adapting to actual system behavior
Solution Approach 2:
The system changes the polling interval parameter dynamically based on observed task completion times. By monitoring actual task durations and adjusting the polling frequency accordingly, the system optimizes resource usage without requiring complex manual configuration or predetermined interval settings
2Loss of time
If frequent polling is performed, then data timeliness is improved, but resource consumption increases
Solution Approach 1:
The system dynamically adjusts polling frequency based on task completion patterns. When tasks complete quickly, polling frequency increases to ensure timely data retrieval. When tasks take longer, polling frequency decreases to conserve resources, achieving an optimal balance between timeliness and resource consumption
Solution Approach 2:
The system uses feedback from actual task completion times to adjust polling intervals. By continuously monitoring whether tasks complete before, at, or after polling intervals, the system learns optimal polling frequencies and adapts its behavior to minimize resource consumption while maintaining data timeliness
3Loss of energy
If low polling frequency is used, then resource consumption is reduced, but data retrieval timeliness deteriorates
Solution Approach 1:
The system implements dynamic polling intervals that automatically increase frequency when tasks complete quickly, ensuring timely data retrieval is maintained. When tasks take longer, the system reduces polling frequency to conserve resources, achieving optimal balance through adaptive behavior
4Adaptability or versatility
If static polling intervals are used, then system complexity is minimized, but adaptability to varying task completion times deteriorates
Solution Approach 1:
The system performs self-configuration by automatically learning optimal polling intervals from observed task completion patterns. No manual intervention or complex configuration is required - the system monitors its own performance and adjusts polling frequencies autonomously, achieving high adaptability without increasing operational complexity
Solution Approach 2:
The system dynamically changes polling interval parameters based on observed task completion times. By automatically adjusting the polling frequency parameter in response to varying task durations, the system achieves adaptability while keeping the configuration mechanism simple and self-managing
Data Source
AI summary
Techniques for facilitating efficient polling using machine learning are disclosed. A system uses historical data associated with execution of tasks to train a machine learning model to predict execution times. After receiving a request for execution of a task, the system provides a polling configuration to the requesting device that includes a polling frequency based on a prediction for when the task execution will be completed. This prediction is generated by the machine learning model.