Dynamic Data Pipeline Scheduling via Complexity and Execution Time
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Solution Overview
Problem
Existing data pipeline management systems face challenges in optimizing resource usage and ensuring data freshness, often leading to over-allocation of resources and missed service level agreements (SLAs).
Innovation Solution
A computer-implemented method that generates a complexity score and predicts execution time for data pipeline jobs, using these metrics to calculate a priority score. This priority score is then used to schedule data pipeline jobs, optimizing resource usage and ensuring data freshness.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If data pipeline jobs are scheduled using traditional methods, then resource allocation is simplified, but resource over-allocation occurs and service level agreements are missed
Solution Approach 1:
The system transforms the scheduling approach by changing parameters from static resource allocation to dynamic priority-based scheduling. It introduces complexity scores, predicted execution times, and priority scores as adjustable parameters that adapt to job characteristics, enabling optimized resource allocation that meets service level agreements while improving resource usage efficiency.
Solution Approach 2:
The system implements feedback mechanisms by continuously monitoring job execution characteristics and using machine learning models to predict execution times. These predictions feed back into the priority score calculation, creating a closed-loop system that adjusts scheduling decisions based on actual performance data, thereby improving both reliability and resource efficiency.
2Productivity
If data pipeline jobs are scheduled without complexity assessment, then scheduling is faster, but resource optimization is poor
Solution Approach 1:
The system enables self-service scheduling by automatically calculating complexity scores and predicted execution times using machine learning models. The scheduling system assesses job characteristics and determines optimal schedules without manual intervention, maintaining high scheduling efficiency while incorporating sophisticated complexity assessment through automated analysis of job parameters.
3Loss of information
If static scheduling is used, then system complexity is low, but data freshness is not ensured
Solution Approach 1:
The system transitions from static to dynamic scheduling by continuously calculating priority scores based on current job characteristics, predicted execution times, and resource availability. This dynamic approach ensures data freshness by adapting scheduling decisions to changing conditions, while the modular architecture manages system complexity through reusable machine learning models and standardized priority calculation mechanisms.
Data Source
AI summary
A computer-implemented method, including: receiving, by a computing device, a data pipeline job request; generating, by the computing device, a complexity score for the data pipeline job request; determining, by the computing device, a predicted execution time for the data pipeline job request; generating, by the computing device, a priority score for the data pipeline job request based on the predicted execution time and the generated complexity score; and comparing, by the computing device, the priority score for the data pipeline job request to another priority score to determine a schedule for the data pipeline job request.