Dynamic Pipeline Parallelism Adjustment via ML Prediction
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Solution Overview
Problem
Current technologies lack the ability to dynamically adjust parallelism in data processing pipelines to optimize performance and meet Quality of Service (QoS) requirements, as the number of parallel stages is typically predetermined and cannot be changed during execution, and machine learning has not been utilized to predict optimal parallelism configurations.
Innovation Solution
A method that uses machine learning to dynamically identify and modify parallelism in data processing pipelines by measuring actual execution times, spawning additional instances when necessary, and training decision tree models to predict behavior and adjust parallelism based on resource availability and performance metrics.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If the number of parallel stages is predetermined at the beginning of execution, then the system configuration is simple and stable, but the system cannot adapt to changing workload conditions and cannot optimize performance dynamically
Solution Approach 1:
The patent implements dynamic parallelism by allowing the number of parallel pipeline stages to change during execution based on monitored performance metrics and workload conditions. The system transitions from a static predetermined configuration to a dynamic adaptive configuration where parallelism levels are adjusted in response to actual runtime conditions, resolving the contradiction between simplicity and adaptability.
Solution Approach 2:
The system incorporates feedback mechanisms that monitor execution progress, performance metrics, and workload characteristics, then use this information to dynamically adjust the number of parallel stages. This closed-loop control enables the system to adapt to changing conditions while maintaining manageable complexity through automated decision-making based on observed performance.
2Productivity
If additional parallel instances are spawned to improve throughput, then processing speed increases, but resource consumption increases and may impact Quality of Service
Solution Approach 1:
The patent dynamically changes the parameter of parallelism (number of instances) based on monitored performance metrics and resource availability. By adjusting this parameter in response to actual system state rather than using a fixed high-parallelism configuration, the system achieves high throughput when needed while conserving resources when workload demands are lower, thus resolving the contradiction between productivity and resource consumption.
3Productivity
If machine learning is used to predict optimal parallelism configuration, then performance optimization is improved, but system complexity and computational overhead increase
Solution Approach 1:
The patent applies machine learning selectively to predict parallelism configuration only when beneficial, rather than using it for all system decisions. This partial application of ML provides performance optimization where needed while avoiding the full computational overhead and complexity of ML-based control for every aspect of system management, resolving the contradiction between optimization benefit and system complexity.
Data Source
AI summary
In an approach to dynamically identifying and modifying the parallelism of a particular task in a pipeline, the optimal execution time of each stage in a dynamic pipeline is calculated. The actual execution time of each stage in the dynamic pipeline is measured. Whether the actual time of completion of the data processing job will exceed a threshold is determined. If it is determined that the actual time of completion of the data processing job will exceed the threshold, then additional instances of the stages are created.