Cloud Infrastructure Drift Remediation via Configuration Graphs
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Solution Overview
Problem
Cloud infrastructure management faces challenges in maintaining consistency due to 'drift' issues, where the actual infrastructure deviates from the desired infrastructure defined in infrastructure as code (IAC) configurations, leading to over-provisioning and potential outages, requiring manual intervention for remediation which is operationally cumbersome, especially at scale.
Innovation Solution
A system and method for automatically remediating drift by generating a directed graph of desired and actual infrastructure, matching infrastructure items, and updating the IAC to reflect differences, allowing for automatic generation of updated configuration code that aligns with the actual cloud infrastructure.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If manual intervention is used to remediate drift by editing infrastructure code, then drift can be corrected, but operational burden increases and productivity decreases
Solution Approach 1:
The system automatically detects infrastructure drift and generates remediation code without human intervention. The drift detection module continuously monitors actual infrastructure state against desired state defined in IAC, and when drift is detected, the system autonomously creates update code to correct the drift, eliminating the need for manual code editing while maintaining infrastructure consistency.
Solution Approach 2:
The patent replaces manual mechanical processes (human operators editing code) with an automated computational system. The system uses algorithms to detect drift, analyze differences, and generate remediation code automatically, substituting human cognitive and manual labor with automated software processes that can handle large-scale infrastructure at speeds impossible for human operators.
2Productivity
If IAC code is used to manage cloud infrastructure, then deployment speed increases, but drift occurs causing reliability issues
Solution Approach 1:
The system implements continuous feedback loops where the drift detection module regularly compares actual infrastructure state against the desired state defined in IAC code. When drift is detected, the system generates remediation code and applies it to restore consistency. This closed-loop feedback mechanism ensures that the fast automated deployments enabled by IAC do not compromise infrastructure consistency, as any deviations are automatically detected and corrected.
Solution Approach 2:
The system performs preliminary drift detection and analysis before infrastructure issues can propagate. By continuously monitoring and comparing infrastructure state against IAC definitions, the system identifies drift early and generates remediation code proactively, preventing inconsistent states from causing outages or operational problems. This preliminary action approach maintains reliability while preserving the speed benefits of IAC-based deployment.
3Loss of information
If manual tracking of cloud resources is performed, then resource awareness is achieved, but time consumption increases significantly
Solution Approach 1:
The system replaces manual resource tracking with automated infrastructure monitoring. The drift detection module continuously queries cloud provider APIs to gather actual infrastructure state information, automatically parses and analyzes the data, and compares it against the desired state defined in IAC code. This automated computational approach provides accurate resource tracking information instantaneously, eliminating the time-consuming manual processes of logging into cloud consoles and reviewing resource lists.
Solution Approach 2:
The system creates and maintains a digital copy of the infrastructure state through automated data collection from cloud providers. The drift detection module generates structured representations of actual infrastructure state that mirror the IAC-defined desired state, enabling automated comparison and analysis. This digital copying approach provides accurate resource tracking information without requiring human operators to manually inspect physical or virtual infrastructure, dramatically reducing tracking time while maintaining accuracy.
Data Source
AI summary
Techniques for remediating drift in real cloud infrastructure from desired cloud infrastructure. In one technique, a configuration graph is generated based on first infrastructure configuration code. A deployed state of a cloud infrastructure is generated based on a data schema and a current state of the cloud infrastructure. Resources indicated in the deployed state are matched to nodes in the configuration graph. Based on differences between the resources and the nodes, the configuration graph is updated to generate an updated configuration graph. Based on the updated configuration graph, second infrastructure configuration code is generated that is different than the first infrastructure configuration code.