Prioritizing System Configurations via Clustering
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Solution Overview
Problem
Current validation testing methods for information handling systems are inefficient due to reliance on historical knowledge and lack of data-driven approaches, leading to incomplete coverage of system configurations, especially with new technology components, and are time-consuming and error-prone.
Innovation Solution
A data-driven and machine learning-based method for selecting system configurations for validation testing, which determines all valid configurations, clusters them using techniques like k-means and Kohenon SOM clustering, and scores them based on factors like new, unique, and difficult components to prioritize configurations for testing.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If validation testing is performed on every possible system configuration, then complete coverage of all configurations is achieved, but the time and resources required become prohibitively large
Solution Approach 1:
The patent segments the vast configuration space into manageable clusters based on component compatibility and system architecture. By dividing millions of configurations into smaller groups, the system can prioritize testing of representative configurations from each cluster, achieving comprehensive coverage without testing every single configuration.
Solution Approach 2:
The patent applies partial action by selecting a subset of high-value configurations for testing rather than all possible configurations. The system identifies and prioritizes configurations that provide maximum validation value, performing testing on only the necessary portion of the configuration space to achieve adequate coverage.
2Ease of operation
If historical knowledge and cues from previously tested configurations are used to select test configurations, then the selection process is simplified, but gaps in validation testing occur especially with new technology components
Solution Approach 1:
The patent implements feedback mechanisms that continuously learn from test results and configuration data. The system uses machine learning algorithms to analyze historical testing data, identify patterns, and improve configuration selection over time. This feedback loop ensures that both historical configurations and new technology components are adequately covered in validation testing.
Solution Approach 2:
The system performs self-service by automatically generating and optimizing the test configuration selection without relying solely on historical knowledge. The machine learning model independently analyzes the configuration space, identifies high-value test cases, and prioritizes configurations for validation, reducing dependency on manual expertise while improving completeness.
3Reliability
If a data-driven and machine learning approach is used to prioritize configurations, then optimal coverage and scientific basis for selection are achieved, but the complexity of the selection system increases
Solution Approach 1:
The patent introduces machine learning algorithms as intermediaries between the configuration data and the selection process. These algorithms act as mediators that automatically analyze configuration characteristics, predict test value, and prioritize configurations without requiring complex manual evaluation criteria. The intermediary simplifies the overall system by automating the decision-making process.
Solution Approach 2:
The patent replaces manual, experience-based configuration selection (mechanical system) with automated machine learning algorithms. This substitution eliminates the need for complex human expertise and manual analysis, using computational methods to objectively prioritize configurations based on data-driven insights rather than subjective judgment.
Data Source
AI summary
A system, method, and computer-readable medium are provided that engages in a data-driven and machine learning-based approach to arrive at high-value, system under test configurations for validation. Embodiments determine all the possible configurations for a computer platform, considering the variety of processors, boards, adapters, and the like, and then utilize a pseudo-ensemble clustering methodology that combines a k-means clustering technique with a neural-network based Kohenon self-organizing map competitive clustering technique to associate like configurations, and then utilizes a data-driven scoring methodology on the clustered configurations to prioritize those configurations to be validation tested.