Augmented Decisioning Engine for Automated Production Infrastructure Configuration

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Solution Overview

Problem

Current approaches to non-functional requirement (NFR) testing in production environments are laborious and expensive, requiring significant developer time for testing, analysis, and configuration adjustments.

Innovation Solution

An augmented decisioning engine employing artificial intelligence and machine learning algorithms generates recommendations for production infrastructure configuration based on real-time metrics comparison with expected performance values, enabling automated optimization of computing systems.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Reliability

If manual NFR testing is performed by developers, then testing thoroughness and system reliability are improved, but developer time consumption and labor costs increase significantly

Engineering Contradiction:

Improvesystem reliabilityVSAvoiddeveloper time consumption

Core Design Contradiction:

ReliabilityVSLoss of time

Solution Approach 1:

The system enables self-service automated NFR testing where the testing infrastructure autonomously performs test execution, result analysis, and configuration recommendations without requiring developer intervention. The automated testing engine independently manages the entire testing workflow, freeing developers from manual testing tasks while maintaining comprehensive system reliability validation.

Inventive Principle:

Principle #25Self-service

Solution Approach 2:

Manual developer actions are replaced with an automated testing engine that uses machine learning models and algorithms to perform NFR testing. The system substitutes human mechanical testing processes with computational automation, including automated test case generation, execution, and analysis, thereby reducing developer time while preserving testing thoroughness.

Inventive Principle:

Principle #28Mechanics substitution (Replace mechanical system)

2Reliability

If comprehensive NFR testing is performed, then system performance and safety are improved, but the number of tests required and computing resources needed increase

Engineering Contradiction:

Improvesystem performance and safetyVSAvoidnumber of tests and computing resources

Core Design Contradiction:

ReliabilityVSQuantity of substance

Solution Approach 1:

The system dynamically adjusts testing parameters such as test scope, resource allocation, and test depth based on system characteristics, risk assessments, and historical data. By changing parameters adaptively rather than using fixed comprehensive testing, the system achieves adequate performance and safety validation with optimized resource consumption and reduced test quantity.

Inventive Principle:

Principle #35Parameter changes

Solution Approach 2:

The system applies partial testing actions focused on critical paths and high-risk areas rather than exhaustive testing of all system components. By concentrating testing efforts on the most impactful areas that determine system performance and safety, the system achieves effective validation with fewer tests and reduced computing resource requirements.

Inventive Principle:

Principle #16Partial or excessive action

3Manufacturing precision

If developer analysis of test results is performed manually, then configuration accuracy is improved, but the time required for analysis and configuration adjustment increases

Engineering Contradiction:

Improveconfiguration accuracyVSAvoidanalysis and configuration time

Core Design Contradiction:

Manufacturing precisionVSLoss of time

Solution Approach 1:

Manual developer analysis of test results is replaced with automated result analysis engines that use machine learning algorithms to interpret test outcomes, identify configuration issues, and generate optimization recommendations. The system substitutes human analysis with computational automation, maintaining configuration accuracy while dramatically reducing the time required for result interpretation and configuration adjustment.

Inventive Principle:

Principle #28Mechanics substitution (Replace mechanical system)

Solution Approach 2:

The system implements automated feedback loops where test results are immediately analyzed and fed back into configuration optimization processes. The automated analysis engine continuously monitors test outcomes and adjusts configurations in real-time based on performance metrics, eliminating manual analysis delays and accelerating the configuration optimization cycle while maintaining high accuracy.

Inventive Principle:

Principle #23Feedback

Data Source

PatentUS11860759B2Using machine learning for automatically generating a recommendation for a configuration of production infrastructure, and applications thereof

Publication Date: 2024.01.02 CAPITAL ONE SERVICES LLC

AI summary

Systems, methods and media are directed to automatically generating a recommendation. Data describing a configuration of a production infrastructure is received, the production infrastructure running the system operating in the production environment. One or more metrics data values indicative of a performance of the system operating in the production environment is retrieved. Expected performance values of the system are received. An augmented decisioning engine compares the metrics data values with the expected performance values. The augmented decisioning engine is trained to provide a recommended configuration of the production infrastructure. Based on the comparing, the augmented decisioning engine is trained to improve subsequent recommendations of configuration of the production infrastructure through a feedback process. The augmented decisioning engine is adjusted based on an indication of whether the configuration of production infrastructure satisfies a threshold metric data value in response to the production infrastructure running the system operating in a production environment.