Modular Electronic Device Task Scheduling via Resource Prediction
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Solution Overview
Problem
Modular electronic devices face challenges in efficiently scheduling task operations due to uncertainties in future resource availability and task demands, leading to suboptimal resource utilization and increased costs.
Innovation Solution
The implementation of a method within modular electronic devices to predict future sets of computing resources and tasks, allowing for proactive scheduling and resource negotiation based on predicted availability and costs, enabling efficient task allocation and minimizing costs.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If traditional scheduling methods are used without prediction, then device complexity is reduced, but resource utilization efficiency deteriorates
Solution Approach 1:
The patent applies preliminary action by predicting future resource availability and task demands before actual scheduling decisions are made. The prediction module analyzes historical data and current system state to forecast future conditions, allowing the scheduler to proactively optimize task allocation rather than reactively responding to current state only. This enables better resource utilization by preparing schedules in advance based on anticipated future conditions.
2Productivity
If prediction of future resources is implemented, then task scheduling optimization is improved, but computational overhead increases
Solution Approach 1:
The patent applies partial action by implementing prediction only for critical resources and tasks rather than comprehensively predicting all system parameters. The prediction module selectively forecasts resource availability and task demands based on predefined criteria such as resource importance, task priority, and uncertainty levels. This selective prediction approach optimizes scheduling for key elements while limiting computational overhead by avoiding unnecessary predictions for less critical components.
3Loss of time
If proactive scheduling based on prediction is used, then task completion timeliness is improved, but scheduling algorithm complexity increases
Solution Approach 1:
The patent applies segmentation by dividing the scheduling system into distinct functional modules: a prediction module that forecasts future conditions, a scheduling module that generates task allocations based on predictions, and an execution module that implements the schedules. Each module has specialized responsibilities, with the prediction module handling forecasting using simplified models and the scheduling module focusing on task allocation. This modular segmentation reduces overall algorithmic complexity by localizing complex prediction logic in a dedicated module while keeping the scheduling logic more straightforward.
Data Source
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AI summary
The present disclosure provides modular electronic devices that are capable of predicting future availability of module combinations and associated computing resources and/or capable of predicting future tasks. Based on such predictions, the module or modular electronic device can choose to schedule or delay certain tasks, alter resource negotiation behavior/strategy, or select from among various different resource providers. As an example, a modular electronic device of the present disclosure can identify one or more computing tasks to be performed; predict one or more future sets of computing resources that will be respectively available to the modular electronic device at one or more future time periods; and determine a schedule for performance of the one or more computing tasks based at least in part on the prediction of the one or more future sets of computing resources that will be respectively available at the one or more future time periods.