Data Processing System Alignment With Wasserstein Distance

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Solution Overview

Problem

Existing data processing systems face challenges in maintaining identical performance levels due to deviations in configurations and metrics over time, requiring efficient methods to identify and quantify similarities and differences to ensure optimal operation and functionality.

Innovation Solution

A method involving similarity estimation using Wasserstein distance to generate system distance matrices, allowing for adjustments to be made to data processing systems to align their performance levels, including automatic or manual adjustments based on similarity values.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Adaptability or versatility

If data processing systems operate independently over time, then system autonomy is maintained, but performance deviations and configuration differences accumulate

Engineering Contradiction:

Improvesystem autonomyVSAvoidperformance consistency

Core Design Contradiction:

Adaptability or versatilityVSReliability

Solution Approach 1:

The system implements feedback by continuously monitoring performance metrics and configuration parameters of multiple data processing systems, comparing them against reference systems, and automatically generating adjustment instructions to maintain performance consistency across the fleet

Inventive Principle:

Principle #23Feedback

Solution Approach 2:

The system dynamically adjusts operational parameters such as CPU frequency, memory allocation, and configuration settings based on similarity calculations and performance deviations, allowing systems to adapt their parameters to maintain optimal performance levels

Inventive Principle:

Principle #35Parameter changes

2Reliability

If manual adjustments are made to align system performance, then performance alignment is achieved, but time consumption and operational complexity increase

Engineering Contradiction:

Improveperformance alignmentVSAvoidadjustment time

Core Design Contradiction:

ReliabilityVSLoss of time

Solution Approach 1:

The system performs self-service by automatically calculating similarity metrics, identifying performance deviations, and generating adjustment instructions without requiring manual intervention, enabling the fleet to self-correct performance issues

Inventive Principle:

Principle #25Self-service

Solution Approach 2:

The system performs preliminary actions by continuously monitoring and calculating similarity metrics before significant performance deviations occur, allowing proactive adjustments to be made before performance degradation impacts service quality

Inventive Principle:

Principle #10Preliminary action

3Measurement precision

If detailed system monitoring and comparison is performed, then performance alignment accuracy is improved, but computational overhead and processing time increase

Engineering Contradiction:

Improvesimilarity measurement accuracyVSAvoidprocessing complexity

Core Design Contradiction:

Measurement precisionVSDevice complexity

Solution Approach 1:

The system segments the monitoring and comparison process into distinct modules: data collection, similarity calculation, deviation analysis, and adjustment generation, allowing each component to be optimized independently and processed in parallel

Inventive Principle:

Principle #1Segmentation

Solution Approach 2:

The system extracts only the most relevant performance metrics and configuration parameters for comparison, filtering out unnecessary data to reduce computational overhead while maintaining measurement precision for critical system attributes

Inventive Principle:

Principle #2Taking out (Extraction)

Data Source

PatentUS20250335181A1Data processing system management using distance matrices

Publication Date: 2025.10.30 DELL PROD LP

AI summary

Methods and systems for managing data processing systems are provided. A similarity estimation process may be employed to identify and quantify similarit(ies) and/or difference(s) between two or more data processing systems in a normalized and quantitative manner. Such similarit(ies) and/or difference(s) may be used to determine whether adjustments to the data processing systems are necessary.