Pipeline Configuration System for Framework Optimization
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Solution Overview
Problem
Pipeline developers face challenges in efficiently configuring underlying frameworks for data processing pipelines, often requiring extensive expertise and leading to performance and cost inefficiencies.
Innovation Solution
A system and method that utilize a trained machine-learning model to automatically generate configuration settings for frameworks based on operation configuration settings, optimizing performance and cost by determining non-transformable, transformable, and internal properties.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If developers manually configure framework settings for data processing pipelines, then they can achieve precise control over performance and cost parameters, but the complexity and time required for configuration increases significantly
Solution Approach 1:
The patent introduces an intermediary configuration system that automatically translates high-level pipeline specifications into detailed framework configuration settings. This mediator handles the complexity of underlying framework parameters (memory, cores, threads) by generating appropriate configurations based on pipeline requirements, thereby reducing developer burden while maintaining precision.
Solution Approach 2:
The configuration system performs self-service by automatically determining optimal framework settings without requiring developer expertise in underlying framework parameters. The system serves itself by generating configurations that satisfy performance and cost targets through automated analysis of pipeline operations and their resource requirements.
2Productivity
If developers manually optimize framework configurations to meet performance and cost targets, then performance and cost efficiency improves, but the time and expertise required for configuration increases
Solution Approach 1:
The system performs preliminary action by pre-configuring framework settings based on pipeline specifications before execution. It analyzes pipeline operations, data volumes, and resource requirements in advance to generate optimized configurations, eliminating the need for developers to manually tune parameters during deployment and reducing configuration time significantly.
Solution Approach 2:
The system automatically adjusts framework parameters (memory allocation, core count, thread configuration) based on pipeline requirements and performance targets. By dynamically changing these parameters according to specific pipeline needs, the system achieves optimized processing efficiency without requiring manual intervention or expertise in parameter tuning.
3Adaptability or versatility
If comprehensive framework configuration options are provided for all operations, then flexibility and adaptability of the pipeline increases, but the difficulty of configuration and expertise required increases
Solution Approach 1:
The configuration system segments the complex configuration process into distinct layers: pipeline-level high-level parameters that developers specify, and framework-level detailed parameters that are automatically generated. This segmentation allows developers to work with simple, intuitive parameters while the system handles the complexity of underlying framework configurations, maintaining flexibility without increasing operational difficulty.
Data Source
AI summary
Data processing tools often use pipeline-based workflows, which consist of a sequence of operations. Each operation is configured according to configuration settings provided by a pipeline developer. The operations may use other software components such as frameworks that may also be configured. An application developer that defines a series of operations to be performed to achieve a desired result is able to provide the configuration settings for the operations. However, the application developer may not have the expertise to efficiently define configuration settings for the underlying frameworks. As discussed herein, a pipeline configuration system is used to generate configuration settings for frameworks used by a pipeline based on configuration settings for the operations of the pipeline. The operation configuration may include non-transformable properties, transformable properties, and internal properties. The pipeline configuration system may primarily modify the transformable properties.