Multi-Phased Data Scheduler for Concurrent Operation Windows
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Solution Overview
Problem
Existing data operation scheduling systems face challenges as the number of operations increases, leading to reduced availability of calendar timeframes and inefficiencies due to non-overlapping execution requirements, which limits the ability to complete multiple operations concurrently.
Innovation Solution
A multi-phased scheduler system that splits data operations into phases, identifies overlap and concurrency potential, and optimally schedules these phases within available timeframes, considering conflicts and resource requirements to maximize scheduling efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If data operations are scheduled with non-overlapping timeframes to ensure proper execution, then execution reliability is maintained, but productivity decreases as fewer operations can be completed concurrently
Solution Approach 1:
The patent segments data operations into multiple phases (e.g., preparation phase, execution phase, completion phase). This allows different operations to overlap in time by scheduling their phases independently, while maintaining reliability through phase-level conflict detection and resolution. The segmentation enables concurrent execution of non-conflicting phases from different operations.
Solution Approach 2:
The scheduling system dynamically adjusts operation timing by identifying and resolving phase conflicts in real-time. It allows operations to transition from rigid non-overlapping schedules to flexible overlapping schedules by detecting conflicts between phases and applying resolution strategies, thereby improving productivity while maintaining execution reliability.
2Productivity
If the number of scheduled operations is increased to improve productivity, then more work is completed, but timeframe availability for each operation is reduced
Solution Approach 1:
By dividing operations into phases, the system can pack more operations into available timeframes by overlapping non-conflicting phases. This increases the number of operations completed without reducing the actual timeframe availability, as the phases are scheduled to utilize idle periods and concurrent execution windows.
Solution Approach 2:
The system maintains continuous utilization of available timeframes by scheduling phases to overlap and run concurrently when no conflicts exist. This eliminates idle time between operations and ensures that the system continuously performs useful work, thereby increasing productivity without compromising timeframe availability.
3Device complexity
If operations are scheduled sequentially without overlap to simplify scheduling, then scheduling complexity is reduced, but loss of time increases due to idle gaps between operations
Solution Approach 1:
Segmenting operations into phases creates a modular scheduling approach that balances complexity and efficiency. The phase-level granularity allows for automated conflict detection and resolution, preventing excessive complexity while enabling overlapping schedules that eliminate idle time between operations.
Solution Approach 2:
The scheduling system incorporates feedback mechanisms that automatically detect phase conflicts and adjust schedules accordingly. This feedback loop manages scheduling complexity by handling conflicts programmatically rather than requiring manual intervention, while simultaneously maximizing resource utilization by minimizing idle time through intelligent phase overlap.
Data Source
AI summary
Various implementations disclosed herein include determining a timeframe for a data operation, splitting the data operation into phases, determining which phases can be concurrently performed with a phase of a different operation, and scheduling the operations based on both the timeframe and the determined concurrency.