ML Resource Scheduler for Real-Time Task Adaptation
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Solution Overview
Problem
Existing resource scheduling systems face challenges such as inability to adjust schedules in real-time, computational lag, model drift, and failure to account for time-dependent task success likelihood, leading to non-optimal resource allocation and task management.
Innovation Solution
A scheduler system that uses multiple memories with different data access times, separate feature extraction and featurization operations, and model evaluators to update schedules based on time-dependent features and changing priorities, employing supervised machine learning techniques like gradient boost trees to determine task likelihood and resource availability.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If traditional scheduling systems are used, then schedule stability is maintained, but real-time adaptability deteriorates
Solution Approach 1:
The system segments scheduling decisions into individual task-level predictions using independent machine learning models for each task, allowing real-time updates without recalculating the entire schedule. Each task's likelihood of successful completion is predicted separately based on current features, enabling modular and flexible schedule adaptation.
Solution Approach 2:
The patent replaces traditional rule-based scheduling mechanics with machine learning models that automatically learn optimal scheduling patterns from historical data. The supervised learning models substitute manual scheduling rules with data-driven predictions, enabling real-time adaptability while reducing the complexity of manually maintaining scheduling logic.
2Measurement precision
If comprehensive feature analysis is performed, then task success prediction accuracy is improved, but computational speed deteriorates
Solution Approach 1:
The system performs preliminary feature extraction and selection before the actual scheduling decision. By pre-processing and organizing task features in advance, the machine learning models can make accurate predictions faster when real-time scheduling decisions are needed, reducing computational lag during critical scheduling moments.
Solution Approach 2:
The system dynamically adjusts the level of feature analysis based on task priorities and time constraints. For high-priority time-sensitive tasks, the system uses a streamlined feature set for faster prediction, while for less critical tasks, comprehensive feature analysis is performed to maximize prediction accuracy.
3Reliability
If machine learning models are continuously updated, then model accuracy is maintained, but computational resource consumption increases
Solution Approach 1:
The system implements feedback mechanisms where actual task outcomes are fed back to retrain and refine the machine learning models. This continuous feedback loop maintains model accuracy by incorporating real-world performance data, allowing the models to adapt to changing patterns while optimizing computational resource usage through efficient retraining strategies.
Solution Approach 2:
Instead of continuous model updates, the system employs periodic retraining at strategically determined intervals. This approach maintains model accuracy by refreshing models based on accumulated data while significantly reducing computational resource consumption compared to continuous updates, balancing reliability with resource efficiency.
4Productivity
If resource allocation is optimized, then task completion likelihood is improved, but resource flexibility deteriorates
Solution Approach 1:
The system applies optimized resource allocation selectively to high-priority tasks where the likelihood of successful completion is most critical. For lower-priority tasks, the system maintains more flexible resource assignment, allowing resources to be reallocated as needed. This partial optimization approach improves overall task completion likelihood while preserving resource flexibility for adaptive responses to changing conditions.
Data Source
AI summary
Generally discussed herein are devices, systems, and methods for scheduling tasks to be completed by resources. A method can include identifying features of the task, the features including a time-dependent feature and a time-independent feature, the time-dependent feature indicating a time the task is more likely to be successfully completed by the resource, converting the features to feature values based on a predefined mapping of features to feature values in a first memory device, determining, by a gradient boost tree model and based on a first current time and the feature values, a likelihood the resource will successfully complete the task, and scheduling the task to be performed by the resource based on the determined likelihood.