Infrastructure Modeling Service for Computing Resource Drift Detection
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Solution Overview
Problem
As data centers grow, managing and synchronizing computing resources becomes complex due to 'out-of-band' modifications that cause configuration drift, leading to operational issues and failures when updating computing resource stacks.
Innovation Solution
An infrastructure modeling service detects configuration drift by comparing expected and actual settings, allowing users to create an updated infrastructure template that reflects the current state of computing resources, ensuring synchronization and preventing update failures.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If virtualization technologies are used to share computing resources among multiple customers, then resource utilization efficiency is improved, but configuration management complexity increases due to out-of-band modifications
Solution Approach 1:
The system implements automated drift detection that continuously monitors computing resources and compares actual configurations against expected configurations from infrastructure templates. When configuration drift is detected, the system generates drift detection results and notifications, enabling automated feedback loops that maintain configuration consistency without manual intervention
Solution Approach 2:
The infrastructure modeling service automatically detects and reports configuration drift without requiring manual inspection or intervention. The system self-monitors the state of computing resources, compares them against templates, and generates drift reports, enabling the system to manage its own configuration consistency
2Adaptability or versatility
If out-of-band modifications are made to computing resources, then operational flexibility is improved, but configuration consistency deteriorates causing drift between resources
Solution Approach 1:
The system establishes expected configurations in advance through infrastructure templates before any modifications occur. These templates define the baseline state of computing resources, enabling the system to detect and report drift when out-of-band modifications are made, thus maintaining configuration consistency while allowing operational flexibility
3Extent of automation
If infrastructure templates are used to provision computing resources, then provisioning automation is improved, but update reliability deteriorates when configuration drift occurs
Solution Approach 1:
The system implements drift detection that provides feedback about configuration inconsistencies before updates are applied. By comparing actual resource states against infrastructure templates and generating drift detection results, the system enables informed update decisions that maintain reliability while preserving automation
Solution Approach 2:
The system performs configuration drift detection before executing updates based on infrastructure templates. This preliminary detection identifies potential conflicts or inconsistencies that would compromise update reliability, allowing the system to address issues before automated updates are applied
Data Source
AI summary
This disclosure describes techniques for resolving discrepancies that occur to interrelated computing resources from computing resource drift. Users may describe computing resources in an infrastructure template. However, computing resource drift occurs when “out-of-band” modifications are made to the computing resources and are not reflected in the infrastructure template. To resolve discrepancies between the infrastructure template and the out-of-band modifications to the computing resources, a notification may be output to a user account associated with the computing resources detailing the differences. An updated infrastructure template may be received that resolves the differences, such as by including configuration settings that reflect a current state of the computing resources. The computing resources may then execute a workflow using the updated template, such that the workflow is executed on all of the computing resources in a current state.