Cloud Resource Tagging Using Utilization Clustering
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Solution Overview
Problem
Manual resource tagging in cloud environments is labor-intensive, prone to errors, and does not scale well with the dynamic nature of resource provisioning and deprovisioning, leading to inefficiencies and increased costs in managing shared cloud resources.
Innovation Solution
An automated method and system for resource tagging that monitors usage data, determines utilization relationships, generates clusters, and assigns tags based on these relationships, using machine-learning models to handle high-dimensional data and ensure accurate, real-time tagging.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If manual resource tagging is used, then resource management can be performed with simple processes, but labor intensity increases and scalability deteriorates
Solution Approach 1:
The system enables self-service by having resources automatically tag themselves based on their utilization relationships. The automated tagging system monitors usage data, determines resource relationships, and assigns tags without requiring manual intervention, thereby eliminating labor intensity while maintaining operational simplicity.
Solution Approach 2:
The patent replaces the mechanical manual tagging process with an automated computer-based system. The system uses software components including a processor set, memory, and clustering algorithms to automatically determine resource relationships and assign tags, substituting human labor with automated mechanical/electronic processes.
2Device complexity
If manual resource tagging is used, then implementation complexity can be kept low, but error rate increases and reliability decreases
Solution Approach 1:
The system incorporates feedback mechanisms where usage data is continuously monitored and fed back into the tagging process. The automated system adjusts tags based on real-time utilization relationships, ensuring high accuracy through continuous verification and correction of resource assignments.
Solution Approach 2:
The system performs preliminary actions by pre-establishing utilization relationships and monitoring frameworks before actual tagging occurs. Resource data is collected and analyzed in advance, allowing the system to make accurate tagging decisions automatically, thereby reducing errors before they occur.
3Device complexity
If manual resource tagging is used, then system simplicity is maintained, but adaptability to dynamic resource changes deteriorates
Solution Approach 1:
The system embodies dynamics by continuously adapting to changing resource conditions. The automated tagging system monitors real-time usage data and utilization relationships, dynamically adjusting tags as resources are provisioned, deprovisioned, or reassigned. This dynamic response capability allows the system to adapt to changing cloud environments without manual reconfiguration.
4Measurement precision
If automated tagging with clustering is implemented, then tagging precision improves, but computational complexity increases
Solution Approach 1:
The system applies segmentation by dividing the complex data processing task into manageable segments. The processor set segments resource data into individual utilization relationships, processes them through clustering algorithms, and generates discrete tags for each resource. This segmentation allows high precision through detailed analysis while managing computational complexity through structured processing steps.
Data Source
AI summary
Automated resource tagging based on utilization relationships includes monitoring usage data associated with each of a plurality of resources and determining resource data associated with each of the plurality of resources based on the usage data. Based on the resource data, one or more utilization relationships for each of the plurality of resources are determined. Further, one or more clusters are generated based on the one or more utilization relationships and the resource data. Each of the one or more clusters includes at least one resource of the plurality of resources. Tag data for each of the plurality of resources is determined based on a corresponding cluster from the one or more clusters and the tag data for each of the plurality of resources is stored.