Tag Recommendation Engine for Cloud Resource Standardization
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Solution Overview
Problem
Inconsistent resource tagging across multiple cloud providers and environments makes it difficult to track and manage cloud resources effectively, especially in multi-cloud environments due to lack of visibility and differing tagging criteria.
Innovation Solution
A tag recommendation engine that utilizes a resource discovery manager, database manager, and machine learning prediction model to standardize resource tagging by discovering resource data, categorizing it into service design and subscription request levels, and providing context-sensitive user-interface prompts for consistent tagging across multiple cloud providers.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual resource tagging is performed across multiple cloud providers, then resource identification and tracking are achieved, but tagging consistency and management efficiency deteriorate due to lack of visibility and differing tagging criteria
Solution Approach 1:
The patent introduces a centralized tagging management system that acts as an intermediary between multiple cloud providers and the organization's resource management processes. This system provides a unified interface for discovering, managing, and standardizing tags across diverse cloud environments, eliminating the need for manual tagging while maintaining consistency. The intermediary translates organization-wide tagging policies into provider-specific implementations, resolving the contradiction between tagging consistency and management complexity.
Solution Approach 2:
The system implements automated feedback loops that continuously monitor tag assignments across cloud resources, compare them against defined policies, and provide real-time corrections or alerts for non-compliant tagging. This automated feedback mechanism ensures tagging consistency is maintained without requiring manual intervention, thereby reducing management complexity while improving measurement precision of tag consistency.
2Productivity
If automated tagging systems are implemented, then tagging efficiency is improved, but adaptability to different cloud provider criteria deteriorates
Solution Approach 1:
The patent implements local quality by allowing the centralized tagging system to apply different tagging strategies and criteria tailored to each specific cloud provider's requirements and capabilities. While maintaining a unified organization-wide tagging framework, the system adapts local tagging implementations to match each provider's specific criteria, data structures, and limitations, thereby achieving both high productivity and cloud provider adaptability.
Solution Approach 2:
The system dynamically adjusts tagging parameters such as tag formats, data types, and validation rules based on the target cloud provider's requirements. This parameter adaptation allows the automated tagging system to efficiently generate tags according to organization policies while simultaneously conforming to the specific technical constraints and preferences of different cloud providers, resolving the contradiction between efficiency and adaptability.
3Loss of information
If comprehensive resource discovery is performed across all cloud environments, then visibility and tracking capability are improved, but system complexity and data processing requirements worsen
Solution Approach 1:
The patent extracts and separates the resource discovery functionality into independent, specialized components that interface with each cloud provider separately. Rather than implementing a single monolithic discovery system, the architecture extracts discovery agents or connectors for each cloud environment, which independently collect resource information and return it to the central management system. This extraction approach improves resource visibility across all clouds while reducing overall system complexity by isolating provider-specific complexity into separate, manageable components.
Data Source
AI summary
A resource recommendation system is described to recommend and standardize resource tagging in a networked computing environment. In one example, cloud resources and related data are discovered, a database of the discovered information is generated, machine learning is applied to the database to build a prediction model, and tags for the resources are recommended, based on the prediction model, at a computing device.