Data Confidence Index for Reliable Computing Asset Selection
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Solution Overview
Problem
Existing systems lack a reliable method to determine the confidence in data stored in a data structure, leading to inefficiencies and inaccuracies in supply chain management, chargeback processes, and hardware allocation.
Innovation Solution
A confidence module determines a confidence index for network assets by monitoring communication, physical scans, network management, and asset states, adjusting the index based on various factors such as communication frequency, physical scan frequency, and asset states, and disqualifying assets with low confidence from participation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If a data confidence index system is implemented to improve data reliability, then data reliability is improved, but device complexity increases
Solution Approach 1:
The patent segments the confidence determination process into distinct modules: a confidence module that determines confidence indexes, a monitoring module that tracks communication and physical scans, and a disqualification module that filters low-confidence assets. This modular segmentation allows the system to improve data reliability through structured confidence assessment while managing complexity through organized functional divisions.
Solution Approach 2:
The system performs preliminary confidence assessment before assets participate in network operations. By determining confidence indexes in advance and disqualifying low-confidence assets beforehand, the system ensures that only high-confidence data sources contribute to subsequent operations, thereby improving overall data reliability without requiring complex real-time verification during operations.
2Reliability
If monitoring and adjustment mechanisms are added to maintain high confidence indexes, then data reliability is improved, but loss of time increases
Solution Approach 1:
The confidence module operates autonomously to determine and maintain confidence indexes without requiring manual intervention. The system automatically monitors communication patterns, physical scan results, and network management data to adjust confidence levels, thereby maintaining high data reliability while minimizing time loss through automated self-service operations.
Solution Approach 2:
The system implements continuous feedback loops where monitoring results directly influence confidence index adjustments. By immediately processing monitoring data and adjusting confidence levels based on observed patterns, the system maintains accurate data confidence measurements while reducing time delays through real-time feedback rather than periodic batch processing.
3Measurement precision
If assets are disqualified based on low confidence indexes, then measurement precision is improved, but productivity decreases
Solution Approach 1:
The confidence index system dynamically adjusts asset participation status based on observed performance and confidence levels. Rather than permanently disqualifying assets, the system allows dynamic re-entry into network operations when confidence indexes improve, thereby maintaining measurement precision through selective disqualification while preserving productivity by retaining the potential for future asset participation.
Data Source
AI summary
Techniques for using a data confidence index are presented herein. In one embodiment, a method includes maintaining, by a server system, confidence index values corresponding to a set of computing assets that are available to the server system to provide a computing service over a network to remote client devices. The confidence index values are indicative of a readiness of the corresponding computing assets in providing the computing service. The method further includes receiving from a first client device a request for a computing resource associated with the computing service and comparing one or more first confidence index values for a first computing asset of the set of computing assets to one or more second confidence index values for a second computing asset of the set of computing assets. The method also includes selecting data from the one of the first computing asset of second computing asset having confidence index values indicating a greater readiness for providing data and using the selected data to respond to the request.