Dynamic Operation Distribution Across Execution Environments
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Solution Overview
Problem
ETL tools often preempt user selections for data processing operations based on presumed execution environment status, which may become outdated, leading to suboptimal performance objectives due to real-time changes in environment conditions.
Innovation Solution
A system comprising an optimizer, dispatcher, and scheduler that determines and adjusts the distribution of operations across execution environments in real-time to achieve performance objectives, using a non-transitory computer-readable medium to execute instructions that assess and adapt to the actual status of execution environments.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If ETL tools use presumed execution environment status to determine operation distribution, then the system complexity is reduced and ease of operation is improved, but the performance objective deteriorates due to outdated environment information
Solution Approach 1:
The system continuously monitors the actual status of execution environments and uses this real-time feedback to adjust operation distributions. The feedback mechanism compares presumed status with actual status and triggers redistribution when performance objectives are not met, resolving the contradiction by maintaining ease of operation while improving productivity through dynamic adaptation.
Solution Approach 2:
The system transitions from static operation distribution based on presumed status to dynamic distribution that adapts to real-time environment changes. The dispatcher continuously evaluates actual execution environment status and redistributes operations dynamically, maintaining ease of operation while optimizing performance objectives through continuous adaptation.
2Productivity
If ETL tools automatically adjust operation distribution based on real-time environment status, then the performance objective is improved, but the device complexity increases due to additional monitoring and adjustment mechanisms
Solution Approach 1:
The system implements self-service through automated monitoring and adjustment mechanisms that operate without user intervention. The optimizer and dispatcher automatically detect performance deviations and redistribute operations based on real-time environment status, improving performance objectives while managing complexity through automation rather than manual processes.
Solution Approach 2:
The system performs preliminary monitoring of execution environment status and prepares alternative operation distributions in advance. When performance objectives are not met, pre-computed alternative distributions are rapidly deployed, reducing the complexity of real-time decision-making while maintaining high performance through proactive preparation.
3Device complexity
If ETL tools maintain fixed operation distribution, then the device complexity is minimized, but the adaptability deteriorates when execution environment status changes
Solution Approach 1:
The system transitions from fixed operation distribution to dynamic distribution that automatically adapts to execution environment changes. The dispatcher continuously monitors actual environment status and redistributes operations when necessary, maintaining low complexity through automated processes while significantly improving adaptability to changing conditions.
Solution Approach 2:
The system changes operational parameters such as operation-to-environment mappings based on real-time environment status. When performance objectives are not met, the optimizer modifies distribution parameters and redistributes operations, maintaining simplicity while achieving high adaptability through parameter adjustment rather than structural complexity.
Data Source
AI summary
Disclosed herein are techniques for managing operations. A distribution of operations across a plurality of execution environments is determined in order to achieve a performance objective. Another distribution of the operations is determined, if the status of the execution environments renders the distribution suboptimal or incapable of achieving the performance objective.