Intelligent Quality Assurance Orchestration Tool for Cloud Validation
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Solution Overview
Problem
Validation of the desired state in cloud-based systems is a resource-intensive and time-consuming task prone to errors, lacking scalability and efficiency.
Innovation Solution
An intelligent quality assurance platform auto-discovers the actual state of a cloud environment by comparing it to a desired state, determining deltas, and predicting necessary tests based on historical data, using credentials stored in a vault service to access cloud resources and applications.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional validation techniques are used to verify the desired state of cloud environment, then validation completeness is improved, but validation time increases significantly and resource consumption increases
Solution Approach 1:
The system performs preliminary actions by continuously maintaining an actual state model of the cloud environment that is automatically updated as changes occur. This pre-computed state information is then readily available for comparison against desired state specifications, eliminating the need for time-consuming validation scans and enabling instant verification when specifications change.
Solution Approach 2:
The system creates and maintains a copy of the cloud environment's actual state as a data structure in memory. This state model is continuously synchronized with the real environment through event notifications, allowing rapid comparison against desired state specifications without needing to access or scan the actual cloud resources during validation.
2Reliability
If traditional validation techniques are used to verify the desired state of cloud environment, then validation thoroughness is improved, but computing resource consumption increases
Solution Approach 1:
The system performs preliminary actions by continuously maintaining an actual state model of the cloud environment that is automatically updated as changes occur. This pre-computed state information is then readily available for comparison against desired state specifications, eliminating the need for time-consuming validation scans and enabling instant verification when specifications change.
Solution Approach 2:
The system creates and maintains a copy of the cloud environment's actual state as a data structure in memory. This state model is continuously synchronized with the real environment through event notifications, allowing rapid comparison against desired state specifications without needing to access or scan the actual cloud resources during validation.
3Measurement precision
If manual validation processes are used for cloud environment configuration, then error detection capability is improved, but scalability deteriorates
Solution Approach 1:
The system implements automated feedback mechanisms where the actual state model is continuously compared against desired state specifications. When discrepancies are detected, the system automatically identifies and reports errors, eliminating manual intervention while maintaining high error detection capability and enabling scalable validation across multiple cloud environments.
Solution Approach 2:
The system creates and maintains a copy of the cloud environment's actual state as a data structure in memory. This state model is continuously synchronized with the real environment through event notifications, allowing rapid comparison against desired state specifications without needing to access or scan the actual cloud resources during validation.
Data Source
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AI summary
Implementations include actions of receiving, by an intelligent quality assurance (iQA) platform, a desired state (DS) file including data indicative of a desired state of a cloud computing environment, triggering, by the iQA platform, an auto-discovery process to provide an actual state of the cloud computing environment based on cloud resources instantiated within the cloud environment, and application resources executing within the cloud environment, the auto-discovery process including retrieving first credentials to enable automated access to the cloud computing environment, determining, by the iQA platform, a delta between the actual state, and the desired state, and providing, by the iQA platform, a report including the delta.