Intelligent Polling Configuration Using ML Task Time Prediction
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Solution Overview
Problem
Existing polling mechanisms in computer systems often face inefficiencies due to fixed intervals that fail to adapt to the dynamic nature of task completion times, leading to resource wastage and potential delays in data retrieval.
Innovation Solution
A machine learning model is trained to predict task execution times, allowing for the generation of dynamic polling configurations that adjust polling frequencies based on predicted completion times, optimizing resource utilization and reducing unnecessary polling.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If fixed polling intervals are used, then system simplicity is maintained, but resource efficiency deteriorates due to unnecessary polling and potential delays
Solution Approach 1:
The patent applies dynamics by transitioning from fixed polling intervals to dynamic, adaptive polling intervals. The system continuously adjusts polling frequencies based on real-time task state predictions from machine learning models, allowing the polling mechanism to adapt its behavior to current system conditions rather than following a static schedule.
Solution Approach 2:
The patent changes the parameter of polling interval from a fixed constant to a variable determined by machine learning predictions. The system modifies polling frequency based on predicted task completion times and confidence levels, transforming the polling parameter into a dynamic value that responds to changing system states.
2Loss of information
If frequent polling is performed, then data availability is improved, but resource wastage increases
Solution Approach 1:
The patent implements feedback by using machine learning models to predict task completion times and adjust polling frequencies accordingly. The system receives feedback from ongoing task execution, updates its predictions, and modifies polling behavior to optimize the balance between data availability and resource consumption based on current task states.
Solution Approach 2:
The patent applies partial action by performing polling only when necessary based on predicted task completion probabilities. Instead of continuously polling at maximum frequency, the system adjusts polling intensity to match the actual need for data retrieval, avoiding excessive polling actions when tasks are unlikely to complete soon.
3Productivity
If adaptive polling intervals are implemented, then resource utilization is optimized, but system complexity increases due to machine learning integration
Solution Approach 1:
The patent applies universality by designing the machine learning model to serve multiple functions: predicting task completion times, estimating confidence levels, and guiding polling frequency adjustments. This multi-functional approach consolidates what could be separate complex systems into a single versatile component that handles multiple aspects of adaptive polling control.
4Loss of time
If polling frequency is increased, then timeliness of data retrieval is improved, but resource expenditure increases
Solution Approach 1:
The patent applies preliminary action by using machine learning models to predict task completion times in advance. The system performs preliminary analysis of task characteristics and historical data to forecast when tasks will complete, allowing it to schedule polling actions optimally before actually needing to retrieve data, thus avoiding both premature and delayed polling.
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.