ML-Driven Architecture Design Platform for Cloud Service Selection
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Solution Overview
Problem
Organizations face difficulties in effectively selecting technology services and cloud service providers for software applications due to the complexity of available out-of-box packages and changing price packages, leading to inefficiencies in resource allocation and architectural recommendations.
Innovation Solution
A machine learning-driven architecture design platform that analyzes priorities and features to identify relevant technology services, updates scores based on user selections, and provides interactive tools for selecting cloud service providers, optimizing resource usage and reducing human subjectivity.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual analysis and selection of technology services is performed, then human expertise and flexibility are utilized, but the process is time-consuming and subjective
Solution Approach 1:
The patent replaces manual human analysis with an automated machine learning system that processes priority data and feature data to identify technology services. The ML model objectively evaluates services based on structured criteria, eliminating human subjectivity while maintaining selection quality through algorithmic precision.
Solution Approach 2:
The system enables self-service by allowing organizations to input their own priority data and feature data, which the ML model then processes automatically to generate service recommendations without requiring external expert intervention. This autonomous operation reduces both time and human resource requirements.
2Reliability
If comprehensive analysis of multiple priorities and features is performed, then selection quality improves, but system complexity increases
Solution Approach 1:
The patent segments the complex selection process into distinct components: input module for priority and feature data, machine learning model for analysis, and output module for service recommendations. This modular architecture manages complexity by separating concerns while maintaining comprehensive analysis capabilities through structured data flow between components.
Solution Approach 2:
The machine learning model serves as an intermediary between the input data (priorities and features) and the final service selection. It transforms complex, unstructured input data into structured recommendations, simplifying the overall system while maintaining analysis depth through the ML layer's ability to process multiple parameters simultaneously.
3Adaptability or versatility
If dynamic score updates based on user selections are implemented, then interactivity and adaptability improve, but computational overhead increases
Solution Approach 1:
The patent implements dynamic score updates that adapt to user selections in real-time. When users interact with the system or provide feedback, the ML model recalculates scores based on updated inputs, enabling the system to adapt to changing requirements. This dynamic behavior is achieved through incremental learning or re-inference on updated data subsets, reducing full-system computational overhead.
Solution Approach 2:
The system changes parameters dynamically by adjusting service scores based on user selections and feedback. Instead of reprocessing all data, the system modifies specific score parameters based on new input, reducing computational energy while maintaining adaptability. This parameter-based adjustment allows efficient updates to the recommendation system.
Data Source
AI summary
A device receives priority data identifying priorities relevant to a configuration of an application and receives feature data identifying features related to the priorities. The device identifies technology services based on a machine learning-driven analysis of the priorities and features, and includes data identifying the technology services as part of the reference architecture. The device provides data identifying the reference architecture for display via an interface, and receives data identifying technology services that have been selected by a user. The device updates scores associated with the reference architecture based on the selected technology services. A subset of the scores may be updated to reflect one or more degrees to which one or more cloud service providers offer the selected technology services. The device provides data identifying the updated scores for display via the interface to allow the scores to be used to select a particular cloud service provider.