Quality-Based Priority Scheduling With Soft Guaranteed Iterations
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Solution Overview
Problem
Existing data processing systems face high processing latency due to unpredictable iteration requirements, where lower quality data sets are often delayed, leading to inefficient resource allocation and increased overall latency.
Innovation Solution
Implementing a quality-based priority scheduling system with soft guaranteed iterations, ensuring each data set receives a minimum number of processing iterations before prioritizing higher quality data sets, using a data processing system with a selection circuit that balances iterations based on error metrics and quality metrics.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If equal priority is given to all data sets until completion or retry, then processing is predictable, but processing latency becomes unacceptably large
Solution Approach 1:
The system changes the priority parameter of data sets dynamically based on their quality metrics. High quality data sets (those requiring fewer iterations) are assigned higher priority to be processed first, while low quality data sets are assigned lower priority. This parameter change resolves the contradiction by making processing time predictable for high quality data while allowing the system to handle diverse data quality scenarios efficiently.
Solution Approach 2:
The scheduling system transitions from a static equal-priority approach to a dynamic priority assignment mechanism. The selection circuit continuously evaluates quality metrics and adjusts processing priorities in real-time, allowing the system to adapt to varying data characteristics and minimize overall latency while maintaining predictable processing for each data set.
2Productivity
If lower quality data sets are delayed, then higher quality data sets are processed faster, but lower quality data sets may be kicked out before receiving sufficient iterations
Solution Approach 1:
The system performs preliminary actions by guaranteeing a minimum number of iterations for all data sets before applying quality-based priority scheduling. This preliminary processing ensures that even low quality data sets receive sufficient attention, and only after this guarantee is met does the system prioritize high quality data sets for faster processing, thus resolving the contradiction between productivity and reliability.
Solution Approach 2:
The processing is segmented into two phases: a guaranteed iteration phase where all data sets receive a minimum number of iterations regardless of quality, and a priority-based phase where high quality data sets are processed faster. This segmentation allows the system to ensure processing completeness for all data while maintaining high productivity for quality data sets.
3Loss of time
If quality-based priority scheduling is implemented, then average latency is reduced, but system complexity increases due to quality metric evaluation and dynamic scheduling
Solution Approach 1:
The system employs self-service mechanisms where data sets automatically provide their quality metrics (such as error counts or signal quality indicators) that the selection circuit uses for priority assignment. This self-service approach reduces the need for complex external evaluation mechanisms, thereby reducing system complexity while still achieving quality-based priority scheduling and reduced average latency.
4Reliability
If multiple iterations are performed on all data sets, then processing thoroughness is improved, but overall processing time increases
Solution Approach 1:
The system changes the iteration parameter dynamically based on data set quality. High quality data sets that require fewer iterations are processed with fewer passes through the detector and decoder circuits, while low quality data sets receive the guaranteed minimum iterations. This parameter change based on quality metrics ensures processing thoroughness for data that needs it while minimizing overall processing time by not over-processing high quality data sets.
Data Source
AI summary
The present inventions are related to systems and methods for data processing, and more particularly to systems and methods for priority based data processing with soft guaranteed global processing iterations.