Orchestrator API Combinations for VDI Compliance
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Solution Overview
Problem
In multi-hybrid cloud environments, service providers face challenges in assembling API calls that maximize compliance with data protection regulations such as GDPR, SOX, and PCI DSS, especially when dealing with variable user counts and different user types, requiring a resource-intensive and inefficient approach to manage VDI environments.
Innovation Solution
An orchestrator assembles and tests API combinations to generate a VDI environment, using a trained model to infer the most compliant API combination based on resource and compliance parameters, and deploys a customized VDI environment, which is then load-tested and monitored for real-time data to improve user experience.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional resource-intensive approaches are used to assemble and test API combinations for VDI environments, then compliance with data protection regulations can be achieved, but system efficiency and resource utilization deteriorate
Solution Approach 1:
The system performs preliminary actions by assembling and testing API combinations in advance to generate a trained model that predicts compliant configurations. This model is then used to quickly determine compliant API combinations for specific compliance requirements without repeating the full testing process, thus achieving compliance while improving efficiency.
Solution Approach 2:
The system creates a trained model that captures the compliance characteristics from previously tested API combinations. This model serves as a copy or representation of the compliance knowledge, allowing the system to infer compliant configurations for new scenarios without performing complete re-testing, thereby maintaining reliability while reducing resource consumption.
2Reliability
If multiple API combinations are assembled and tested to ensure compliance, then compliance accuracy improves, but resource consumption and time requirements increase
Solution Approach 1:
The system performs comprehensive API combination testing in advance to build a trained model containing compliance knowledge. When a specific compliance requirement arises, the system uses the pre-trained model to quickly infer the appropriate API combination rather than performing complete testing again, thus maintaining compliance accuracy while significantly reducing time consumption.
Solution Approach 2:
The system dynamically adapts its approach based on the specific compliance requirement. For common compliance scenarios, it uses the pre-trained model for rapid inference. For new or complex requirements, it can perform additional targeted testing and update the model, balancing compliance accuracy with time efficiency through dynamic decision-making.
3Measurement precision
If comprehensive testing of VDI environments is performed, then compliance result accuracy improves, but resource intensity increases
Solution Approach 1:
The system creates a trained model that serves as a compressed representation of comprehensive testing results. This model captures the essential compliance characteristics without requiring the full computational resources of the original testing process. The model can then be applied repeatedly with minimal resource consumption while maintaining measurement precision for compliance assessments.
Solution Approach 2:
The system changes the state of compliance assessment from requiring full re-testing to using inference based on pre-trained models. This parameter change in the assessment approach maintains compliance result accuracy by leveraging the knowledge captured in the model while dramatically reducing the resource intensity of each assessment.
Data Source
AI summary
A method for managing virtual desktop infrastructure (VDI) environments includes: obtaining, by an orchestrator, a resource related parameter and a compliance related parameter; assembling, by the orchestrator, an application programming interface (API) combination to generate a VDI environment based on a plurality of vendor-provided APIs; testing, by the orchestrator and for a compliance result, the VDI environment across a range of users based on the resource related parameter and compliance related parameter; providing, by the orchestrator, the range of users and VDI environment to an analyzer, in which the analyzer is instructed by the orchestrator to generate a model that maximizes the compliance result of the VDI environment; generating, by the analyzer, a trained model by training the model using at least the range of users, VDI environment, compliance related parameter, resource related parameter, and compliance result; and initiating, by the analyzer, notification of an administrator about the trained model.