Automated VM Image Pipeline with Risk-Based Validation
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Solution Overview
Problem
Traditional processes for building and validating Virtual Machine (VM) images in cloud services are slow, inefficient, and disjointed, leading to time-intensive and resource-heavy manual steps, which can cause delays and inefficiencies in delivering updated cloud services.
Innovation Solution
Automating the building and validating of VM product images within a data pipeline, where the process is streamlined by determining the need for testing based on the risk level of updates, reducing the use of computing resources and integrating prerequisites management for unified installation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional manual processes are used for building and validating VM images, then validation thoroughness is improved, but delivery time increases significantly
Solution Approach 1:
The system performs preliminary actions by automatically building VM images in a sandbox environment before formal validation, and by pre-identifying which updates require full validation based on risk assessment. This allows the system to prepare validation candidates in advance, reducing the time needed during the actual delivery process while maintaining thoroughness where needed.
Solution Approach 2:
The system changes the parameter of validation intensity by dynamically adjusting the level of testing based on risk assessment. Low-risk updates receive minimal validation, medium-risk updates receive standard validation, and high-risk updates receive comprehensive validation. This parameter change allows the system to maintain validation thoroughness for critical updates while reducing time loss for routine updates.
2Reliability
If full validation testing is performed on all updated VM images, then validation completeness is improved, but computing resource usage increases
Solution Approach 1:
The system applies local quality by differentiating validation intensity based on the specific characteristics of each update. Instead of uniform validation, the system applies appropriate validation levels locally to each VM image update based on its risk profile. Critical security updates receive comprehensive validation, while minor bug fixes receive minimal validation, optimizing computing resource usage while maintaining validation completeness where needed.
Solution Approach 2:
The system uses partial action by performing only the necessary validation tests for each update rather than running all possible tests. The risk assessment mechanism determines the appropriate scope of validation, allowing the system to perform partial validation for low-risk updates and excessive (comprehensive) validation for high-risk updates, thereby optimizing computing resource usage while maintaining validation effectiveness.
3Adaptability or versatility
If VM images are created and validated in separate locations, then development flexibility is improved, but process integration increases complexity
Solution Approach 1:
The system merges the development and validation processes into a unified automated pipeline. The sandbox environment where VM images are built is directly integrated with the validation system, allowing seamless transition from creation to validation without manual intervention. This merging reduces process integration complexity while maintaining development flexibility through automated orchestration.
Solution Approach 2:
The system uses an automated orchestration layer as an intermediary between the sandbox environment and the validation system. This intermediary automatically manages the transition of VM images between development and validation phases, handling file transfers, environment configuration, and process coordination. This intermediary simplifies the integration complexity by abstracting the communication and coordination between separate locations.
4Ease of operation
If manual initiation steps are used for VM image creation and validation, then process control flexibility is improved, but productivity decreases
Solution Approach 1:
The system implements self-service by automating the entire VM image creation and validation process. The orchestration system automatically initiates building, validates images, assesses risks, and determines validation requirements without manual intervention. This self-service automation dramatically improves productivity while maintaining process control flexibility through configurable parameters and automated decision-making rules.
Solution Approach 2:
The system uses feedback mechanisms to maintain process control flexibility. The risk assessment module continuously monitors update characteristics and provides feedback to the orchestration system, which automatically adjusts validation strategies accordingly. This feedback loop allows the system to respond dynamically to different update scenarios, maintaining the flexibility previously provided by manual control while achieving high productivity through automation.
Data Source
AI summary
Methods, systems, and computing devices for the automation of the building and validating of an updated VM product image within a data pipeline. The disclosed systems can also determine if the updated VM product image should be validated by testing the updated VM product image based on the risk of the update data to the functionality of the updated VM product image. By automating the process of building and validating VM product images within a data pipeline, a system can mitigate the time lag caused by traditional processes, which use manually initiated steps in creating VM images and separately manually initiated tools for validating VM images.