Cloud Task Lock Scheduling via Predictive Run Time
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Solution Overview
Problem
In cloud-computing networks, determining precise automated task run times for resource locking is challenging, leading to extended resource unavailability beyond what is necessary, as current methods often inflate expected durations to avoid conflicts, resulting in inefficient resource allocation.
Innovation Solution
The implementation of predictive modeling using historical run time data to accurately predict task durations, allowing for optimized resource lock scheduling, where the actual run times are used to update the database and improve future predictions, enabling more precise scheduling and reducing unnecessary resource locking.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If expected durations are inflated to avoid conflicts, then reliability of task completion is improved, but resource utilization deteriorates due to extended locking beyond necessity
Solution Approach 1:
The patent applies parameter changes by transitioning from static, inflated duration estimates to dynamic, data-driven predictions. The system collects historical run time data and uses it to continuously refine and update task duration parameters, allowing the locking duration to be optimized based on actual performance patterns rather than conservative fixed estimates.
Solution Approach 2:
The patent implements feedback mechanisms by monitoring actual task run times and using this information to update the predictive model. The system continuously learns from past performance, adjusting future duration predictions based on the feedback loop between actual execution and predicted values, thereby improving both reliability and resource utilization over time.
2Productivity
If precise run time prediction is implemented, then resource utilization is improved, but measurement precision requirements worsen due to need for accurate timing data
Solution Approach 1:
The patent applies preliminary action by collecting and storing historical run time data before making predictions. The system prepares the data infrastructure and accumulates measurement data in advance, so that when prediction is needed, there is already a sufficient dataset to work with, reducing the immediate burden of measurement precision requirements.
Solution Approach 2:
The system performs self-service by automatically collecting, storing, and analyzing its own run time data without requiring external intervention. The predictive model uses internally generated data to improve its own accuracy, creating a self-improving system that reduces measurement precision requirements through accumulated self-observed patterns.
Data Source
AI summary
The present approach relates to systems and methods for facilitating run time predictions for cloud-computing automated tasks (e.g., automated tasks), and using the predicted run time to schedule resource locking. A predictive model may predict the automated task run time based on historical run time to completion, and the run time may be updated using machine learning. Resource lock schedules may be determined for a queue of automated tasks utilizing the resource based on the predicted run time for the various types of automated tasks. The predicted run time may be used to reserve a resource for the given duration, such that the resource is not available for use for another task.