Automated Data Center Tag Management for Accurate Asset Tracking
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Solution Overview
Problem
Existing data center management systems face challenges in accurately associating data tags with data center assets, leading to errors and inefficiencies in managing and monitoring these assets due to user errors, misdefined keys, and lack of correlation between data tags.
Innovation Solution
A system and method for generating, registering, and managing data tags within a data center asset environment, utilizing a data tag management operation to reduce errors, coalesce similar semantic concepts, and correlate related tags, enhancing data center monitoring and management operations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If manual data tagging is performed by users, then data tags can be associated with data center assets, but user errors and misdefined keys lead to inaccuracies in tag association
Solution Approach 1:
The system enables self-service automated data tagging by extracting asset information from monitoring data and automatically generating and associating tags with assets, eliminating the need for manual user input while ensuring accurate tag association through systematic processing
Solution Approach 2:
The system incorporates feedback mechanisms where tag association results are validated and corrected through continuous monitoring and management operations, allowing the system to learn from and correct tagging errors while maintaining operational simplicity
2Adaptability or versatility
If data tags are manually created and managed, then flexibility in tag definition is maintained, but errors in data tagging increase and efficiency decreases
Solution Approach 1:
The system provides a universal automated tagging framework that can handle multiple asset types and tag categories through a single process, maintaining adaptability across different data center assets while significantly improving productivity through automation
Solution Approach 2:
The system dynamically adjusts tagging parameters and tag definitions based on asset characteristics and monitoring data, maintaining flexibility in tag definition while improving efficiency through automated parameter optimization and error reduction
3Reliability
If traditional asset monitoring methods are used, then existing systems can operate, but misdefined keys and lack of correlation between data tags reduce monitoring accuracy
Solution Approach 1:
The system merges data tag management with asset monitoring operations, combining tag generation, association, and validation into a unified process that improves reliability by ensuring consistent and accurate tag-information correlation throughout the monitoring lifecycle
Solution Approach 2:
The system introduces an intermediary data tag management operation that acts as a mediator between raw monitoring data and asset information, validating and correlating tags to prevent information loss and improve monitoring accuracy through systematic verification
4Device complexity
If no centralized tag management operation is implemented, then system complexity is reduced, but errors in data tagging persist and asset management efficiency is compromised
Solution Approach 1:
The system segments tag management into distinct automated operations (tag generation, association, validation) that can be independently executed and managed, reducing overall system complexity while improving reliability through modular error prevention and correction mechanisms
Data Source
AI summary
A system, method, and computer-readable medium for performing a data center monitoring and management operation. The data center monitoring and management operation includes: generating a tag for an object within a data center asset, the tag for the object associating a contextual meaning with the object; registering the tag when the tag is generated; storing the tag within a tag storage repository when the tag is registered; and, managing an aspect of the tag via a data tag management operation.


