Cloud Service Categorization via Metadata Mapping
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Solution Overview
Problem
Manual curation of cloud services by administrators is time-consuming and cumbersome due to the increasing number of cloud networks and services provided by multiple providers, making it difficult to efficiently deploy, provision, and manage computing resources.
Innovation Solution
Implementing a multi-cloud management platform that categorizes cloud services using metadata-based keywords and predefined mappings, allowing for automated grouping of services into functional categories, thereby simplifying access and management for users.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If manual curation of cloud services is performed by administrators, then services can be deployed and managed, but the process becomes time-consuming and cumbersome due to the increasing number of cloud networks and services
Solution Approach 1:
The system enables self-service by automatically categorizing cloud services based on their metadata and functional characteristics. The multi-cloud management platform autonomously analyzes service descriptions, tags, and attributes to group them into functional categories without requiring manual intervention from administrators, thereby eliminating the time-consuming manual curation process while maintaining accurate service organization
Solution Approach 2:
The invention changes the parameter of service organization from manual administrative classification to automated metadata-based classification. By transforming the basis of categorization from human judgment to systematic analysis of service metadata (such as functional characteristics, service types, and technical attributes), the system achieves rapid and consistent service grouping without human intervention
2Adaptability or versatility
If multiple cloud networks and services from different providers are integrated, then service versatility increases, but the complexity of managing and organizing these services increases
Solution Approach 1:
The system segments the diverse cloud services into distinct functional categories based on their metadata and characteristics. By dividing the service landscape into organized groups (such as compute services, storage services, networking services, etc.), the system makes the vast array of multi-cloud services manageable and navigable, reducing the perceived complexity while maintaining comprehensive coverage
Solution Approach 2:
The multi-cloud management platform provides universal functionality by automatically categorizing services from different cloud providers using a unified classification system. This universal approach allows the platform to handle heterogeneous services from multiple providers consistently, applying the same metadata analysis and categorization logic across all cloud networks, thereby simplifying management despite the diversity of underlying services
Data Source
AI summary
Example techniques of cloud service categorization are described. In an example, presence of a keyword, in metadata of an item, amongst a plurality of items, is determined. Each of the items is representative of a corresponding service hosted by a cloud network in a cloud infrastructure. The metadata is indicative of functional characteristics of a service represented by the item. Based on the keyword and a predefined mapping, the item is grouped in a category, amongst a plurality of categories. The predefined mapping is indicative of a correlation between the plurality of keywords and the plurality of categories. A list of the plurality of categories may be generated, where each of the plurality of items is grouped in a respective category amongst the plurality of categories.