Dynamic Job Completion Prediction via Real-Time Task Tracking
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Solution Overview
Problem
Traditional task management systems face challenges in accurately predicting job completion times, especially when system conditions differ from historical executions, and struggle to handle dynamic task creation and resource allocation efficiently.
Innovation Solution
A queue-based task management system that introduces immediate-mode tasks, allowing child tasks to be executed using the resources of their parent tasks, and uses predictive analytics based on current system conditions and task progression to estimate remaining job time.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional queue-based task management is used, then tasks are processed in order, but accurate prediction of job completion time becomes difficult when system conditions change
Solution Approach 1:
The patent implements dynamic prediction that continuously updates job completion time estimates based on current system conditions and real-time task execution progress. Instead of relying on static historical averages, the system adapts to changing resource availability, task creation rates, and execution speeds, making the prediction mechanism flexible and responsive to current system state
Solution Approach 2:
The system incorporates feedback loops that monitor actual task execution performance and use this information to refine completion time predictions. By tracking metrics such as tasks completed per unit time and adjusting predictions based on observed performance, the system continuously improves accuracy while adapting to varying system conditions
2Adaptability or versatility
If tasks are created dynamically during job execution, then the system handles complex workflows, but the number of pending tasks does not accurately reflect remaining work
Solution Approach 1:
The patent introduces an intermediary prediction mechanism that bridges the gap between observed task creation patterns and total job scope. By using statistical analysis of task creation rates and patterns as an intermediary step, the system can estimate the total number of tasks that will ultimately be created, allowing for more accurate remaining work calculation even as tasks are dynamically generated
Solution Approach 2:
The system performs preliminary statistical analysis of task creation patterns early in job execution to establish baseline expectations for total task volume. This preliminary assessment allows the system to adjust its remaining work calculations proactively rather than reactively, improving measurement accuracy before the full scope of dynamic task creation is known
3Measurement precision
If historical execution data is used for predictions, then initial predictions can be made, but predictions do not account for current system state differences
Solution Approach 1:
The patent transitions from static historical-based predictions to dynamic condition-aware predictions. The system continuously monitors current system state metrics such as resource availability, task queue depth, and execution speed, then adjusts predictions to reflect these real-time conditions rather than relying on historical averages that may no longer be relevant
Solution Approach 2:
The system changes the parameters used for prediction from fixed historical values to dynamic parameters that reflect current system state. By adjusting prediction calculations based on real-time metrics such as current task creation rate, resource utilization, and execution speed, the system maintains accuracy across varying operational conditions
4Productivity
If resources are allocated to parent tasks, then child tasks can execute immediately, but resource utilization efficiency may decrease
Solution Approach 1:
The patent segments resource allocation decisions by task hierarchy level. Parent tasks receive dedicated resources to enable immediate child task execution, while the system separately tracks and optimizes overall resource utilization across all tasks. This segmentation allows child tasks to benefit from parent task resources without forcing the entire system to operate at reduced efficiency
Data Source
AI summary
Techniques are provided for predicting time remaining for currently-execution jobs. Rather than predict time remaining based on prior executions, time remaining is predicted based on what has happened so far in the current execution. In order to generate predictions for a currently-executing job instance based on statistics about the currently-executing job instance, the system tracks, for each monitored job instance: (a) how many completed-tasks are currently associated with the job instance, and (b) how many created-but-not-completed tasks are associated with the job instance, and then predicts (c) how many not-yet-created tasks the job instance is likely to have.