Consolidated Performance Metric Analysis for Data Processing Systems
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Solution Overview
Problem
The sheer volume of performance data from computing devices overwhelms users and administrators, making it difficult to utilize effectively, and as computing devices evolve, this issue is expected to worsen without a method to refine and consolidate this data.
Innovation Solution
Performance metrics from multiple data processing elements are grouped into aspect sets, with consolidated performance metrics calculated to reflect changes over time intervals, using weighting factors to signify the significance of each aspect set and impact factors to quantify changes, ultimately leading to the determination of a consolidated performance metric for the performance metric domain.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If performance data is collected from multiple data processing elements, then the quantity and detail of performance information increases, but the volume of data becomes overwhelming and difficult to utilize
Solution Approach 1:
The patent segments performance data by organizing it into multiple performance centers, each representing a specific data processing element or functional area. This segmentation allows the large volume of performance data to be divided into manageable units that can be independently analyzed and consolidated, making the data more usable while preserving comprehensive coverage
Solution Approach 2:
The patent merges performance data from multiple data processing elements by calculating consolidated performance metrics that aggregate information across different performance centers. This merging process combines the quantity and detail of individual performance data into unified metrics that are easier to utilize while retaining the comprehensive information from all sources
2Ease of operation
If performance metrics are consolidated into aggregated measures, then the ease of data utilization improves, but the detail and specificity of performance information is lost
Solution Approach 1:
The patent applies local quality by maintaining different levels of data aggregation with varying degrees of detail. Individual performance centers retain detailed local performance metrics, while consolidated performance metrics provide higher-level summaries. This allows users to access detailed information where needed while utilizing aggregated data for general monitoring, thus preserving information at appropriate levels
3Measurement precision
If performance data is collected at frequent time intervals, then the precision of performance measurement improves, but the volume of data increases and becomes more difficult to manage
Solution Approach 1:
The patent implements partial action by collecting performance data at multiple time intervals but processing and consolidating it in a structured manner. Rather than analyzing every individual data point, the system selectively aggregates data into performance metrics that capture essential performance characteristics, thus maintaining measurement precision while reducing the manageable data volume
Data Source
AI summary
Performance metrics may be received from multiple data processing elements associated with a performance metric domain and a consolidated performance metric may be determined. Grouping the performance metrics into performance metric groups may be performed based on their respective associations with different aspects of the data processing elements.