Connection Configuration Selection for Computing Component Performance
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing information handling systems lack efficient methods to manage and optimize connection configurations of computing components across diverse devices and configurations, leading to suboptimal performance.
Innovation Solution
A method and system for identifying performance metrics, calculating weighted averages and ratios, and selecting optimal connection configurations based on these metrics to align computing components with improved performance.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If connection configurations are managed manually or using traditional methods, then system complexity is reduced, but performance optimization capability deteriorates
Solution Approach 1:
The system automatically identifies performance metrics, calculates weighted averages, determines configuration ratios, and selects optimal connection configurations without manual intervention. The computing component autonomously monitors its own performance and adjusts configurations based on calculated metrics, enabling self-optimization while maintaining system complexity at acceptable levels.
Solution Approach 2:
The system changes connection configuration parameters based on calculated performance metric ratios. By dynamically adjusting configuration parameters according to measured performance data and weighted averages, the system achieves performance optimization while managing complexity through structured parameter management rather than arbitrary changes.
2Productivity
If multiple performance metrics are collected and analyzed, then performance optimization improves, but data processing complexity increases
Solution Approach 1:
The system collects performance metrics, calculates weighted averages, determines configuration ratios, and uses this feedback to select optimal connection configurations. The performance data feeds back into the decision-making process, creating a closed-loop system that continuously optimizes based on actual measured performance rather than static pre-configurations.
Solution Approach 2:
The performance analysis is segmented into distinct components: identifying individual performance metrics, calculating weighted averages for each metric, determining configuration ratios based on multiple factors, and selecting optimal configurations. This segmentation of the data processing task into manageable stages reduces overall complexity while maintaining comprehensive performance optimization.
3Productivity
If connection configurations are optimized for specific performance metrics, then performance improves, but adaptability to different configurations deteriorates
Solution Approach 1:
The system evaluates multiple connection configurations and performance metrics simultaneously, calculating weighted averages across different metric types. The optimization approach is universal, working with any computing component and any connection configuration type, rather than being tailored to specific hardware. This multi-functional approach maintains adaptability while achieving performance optimization.
Solution Approach 2:
The system dynamically adjusts connection configurations based on real-time or near-real-time performance metric analysis. Rather than static optimization for a single configuration type, the system adapts configurations dynamically based on measured performance, allowing it to respond to changing conditions while maintaining optimization efficiency.
Data Source
AI summary
Managing connection configurations of computing components, including identifying performance metrics associated with the information handling system, each information handling system including a particular computing component; calculating a performance metric configuration ratio associated with the particular computing component based on a ratio of the first weighted average and the second weighted average; and calculating an adjusted performance metric configuration ratio based on the performance metric configuration ratio and a performance indicator associated with the performance metric; calculating an optimized performance metric configuration ratio for the computing component based on an average of the adjusted performance metric configuration ratios of the performance metrics; selecting, based on a value of the optimized performance metric configuration ratio, one of the first connection configuration and the second connection configuration; providing instructions to each of the information handling systems indicating the selected connection configuration.