Archetypal Analysis for Data Center Serviceability Assessment
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Solution Overview
Problem
Current methods for assessing data center serviceability, such as discriminant analysis, often result in unrealistic or 'mythical' measurements, being time-consuming and cost-prohibitive, failing to provide accurate and efficient characterization of serviceability characteristics.
Innovation Solution
The implementation of archetypal analysis to identify real-life patterns in data center serviceability, using historical data to create archetypes that do not include mythical measurements, allowing for rapid and cost-effective categorization of serviceability characteristics in target data centers.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If discriminant analysis is used to assess data center serviceability, then statistical fitting of data samples to factors is achieved, but unrealistic or mythical measurements are produced
Solution Approach 1:
The patent changes the fundamental parameter being measured from abstract statistical factors to concrete serviceability characteristics. By transforming the measurement parameters to represent actual physical and operational attributes of data centers (such as accessibility, modularity, and serviceability metrics), the system maintains statistical rigor while ensuring measurements correspond to real-world realities rather than mythical constructs
Solution Approach 2:
The patent creates archetype data centers that are simplified copies or representations of actual data center configurations. These archetypes capture the essential serviceability characteristics without the complexity that leads to mythical measurements. By comparing target data centers against these realistic archetypes rather than abstract statistical factors, the system produces reliable and interpretable results
2Measurement precision
If full-scale professional services engagement is used for assessment, then comprehensive serviceability evaluation is achieved, but cost and time requirements become prohibitive
Solution Approach 1:
The patent segments the comprehensive assessment into distinct, modular components represented by different archetype data centers. Each archetype evaluates specific serviceability dimensions independently. This segmentation allows the assessment to be performed through systematic comparison against multiple archetypes rather than requiring a single lengthy professional engagement, significantly reducing time while maintaining comprehensiveness
Solution Approach 2:
The patent performs preliminary action by pre-defining multiple archetype data centers with known serviceability characteristics before actual assessment. These archetypes are prepared in advance with comprehensive serviceability evaluations already completed. When assessing a target data center, the system simply compares it against these pre-prepared archetypes, eliminating the need to perform full-scale assessment from scratch and dramatically reducing assessment time and cost
3Productivity
If cursory or non-existent assessment is used, then time and cost are reduced, but ability to adequately service the customer is compromised
Solution Approach 1:
The patent creates a universal assessment system using archetype data centers that can evaluate multiple serviceability dimensions simultaneously. Each archetype represents a comprehensive serviceability profile covering accessibility, modularity, and other critical characteristics. By comparing target data centers against these multi-functional archetypes, the system achieves rapid assessment without sacrificing reliability, as each archetype encapsulates comprehensive serviceability knowledge in a single comparable unit
Data Source
AI summary
In one embodiment, a method and apparatus for rapid categorization of data center serviceability characteristics is disclosed. The method includes identifying one or more variables of interest relating to serviceability of data centers, identifying one or more archetypal data center patterns using historical data for the one or more variables of interest, wherein the one or more archetypal data center patterns do not include mythical measurements, collecting data for the one or more variables of interest from a target data center, and determining a best match of the collected data from the target data center to one of the archetypal data center patterns. Other embodiments are also disclosed.