Cloud Configuration Graphs for API Rate Limit Resolution
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Solution Overview
Problem
Cloud computing environments face inefficiencies in monitoring and managing the current and historical configurations of cloud computing resources due to polling-based models that starve API access and complicate the navigation of relationships between resources, leading to duplicative efforts and rate limit issues.
Innovation Solution
A system that automates the capture of configuration changes using a graph-based query system, storing detected changes in a central data storage device and generating relationship graphs to simplify the retrieval of configuration information, reducing the need for individual API polling and enabling efficient data extraction and relationship establishment between cloud computing resources.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If polling-based models are used to retrieve configuration data from cloud computing resources, then current configuration information can be obtained, but API access is starved and rate limit issues occur
Solution Approach 1:
The system performs preliminary actions by establishing event subscriptions with cloud computing resources in advance. Instead of polling for configuration changes, the system registers event handlers that automatically trigger when changes occur, eliminating the need for continuous API polling and preserving API access for other operations.
Solution Approach 2:
The system introduces an intermediary event subscription mechanism between the monitoring system and cloud computing resources. This intermediary layer receives configuration change notifications directly from the cloud resources, decoupling the monitoring system from direct API polling and preventing API starvation.
2Loss of information
If exhaustive API queries are performed to retrieve configuration data, then complete configuration information is obtained, but rate limit or throttling issues result
Solution Approach 1:
The system extracts only the essential configuration change information through event subscriptions rather than performing exhaustive API queries. By subscribing to specific configuration change events, the system obtains necessary information without triggering rate limits associated with comprehensive data retrieval.
Solution Approach 2:
The system applies partial action by subscribing only to configuration change events that are relevant, rather than performing exhaustive queries that retrieve all possible data. This selective approach maintains API access reliability while obtaining sufficient configuration information.
3Loss of information
If individual API calls are executed for each cloud computing resource to define hierarchical relationships, then relationship information is obtained, but the process becomes inefficient and time-consuming
Solution Approach 1:
The system merges relationship information retrieval with configuration change monitoring by using graph-based structures. When configuration changes are detected through event subscriptions, the system automatically updates relationship graphs that already contain contextual information about resource hierarchies, eliminating the need for separate API calls to determine relationships.
Solution Approach 2:
The system performs preliminary action by pre-establishing graph-based relationship structures that map hierarchies between cloud computing resources. These pre-built relationship graphs enable immediate contextual understanding when configuration changes occur, without requiring time-consuming API calls to infer relationships.
4Ease of manufacture
If the flat structure is used to store relationships between cloud computing resources, then data storage is simple, but relationship navigation becomes cumbersome and inefficient
Solution Approach 1:
The system transitions from a flat, two-dimensional data storage structure to a multi-dimensional graph-based structure. This dimensional change enables the system to represent hierarchical relationships, parent-child connections, and contextual associations between cloud computing resources in a way that maintains storage efficiency while dramatically improving relationship navigation and query capabilities.
Data Source
AI summary
Described herein is a system for automatically capturing configuration changes to the cloud computing resources. The system for automatically capturing configuration changes may detect changes to configurations of cloud computing resources across the geographic regions, in real-time. The changes may be stored in a central data storage device instantiated by a central cloud computing account. Furthermore, a relationship graph indicating the relationships between the different cloud computing resources may be generated.