Application Configuration Correction via ML Classifier

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Solution Overview

Problem

In computing environments, especially cloud-based ones, applications often face inefficiencies due to incorrect configurations, leading to excessive resource utilization and suboptimal performance, as users struggle to adjust configurations in response to varying conditions, and migrating applications between environments further complicates proper configuration management.

Innovation Solution

A method involving training a classifier model to identify preferred configurations by analyzing execution metrics and comparing them to expected metrics, allowing for the detection and correction of inefficient configurations without transferring configuration data, thus optimizing resource use and performance.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Productivity

If applications are configured manually by users, then configuration flexibility is maintained, but configuration efficiency and accuracy deteriorate due to user struggle and inability to adjust to varying conditions

Engineering Contradiction:

Improveconfiguration efficiencyVSAvoiduser ability to adjust configurations

Core Design Contradiction:

ProductivityVSEase of operation

Solution Approach 1:

The system enables self-service by having the application automatically detect its own configuration inefficiencies through the classifier model and receive automated corrections, eliminating the need for users to manually adjust configurations and allowing the system to adapt to varying conditions autonomously

Inventive Principle:

Principle #25Self-service

Solution Approach 2:

The system implements feedback by continuously monitoring application execution metrics, comparing them against expected metrics through the classifier model, and automatically adjusting configurations based on the detected deviations, creating a closed-loop control system that improves configuration efficiency

Inventive Principle:

Principle #23Feedback

2Adaptability or versatility

If applications are migrated between computing environments, then deployment flexibility is improved, but configuration management complexity worsens due to environment-specific optimization requirements

Engineering Contradiction:

Improvedeployment flexibilityVSAvoidconfiguration management complexity

Core Design Contradiction:

Adaptability or versatilityVSDevice complexity

Solution Approach 1:

The system handles environment-specific optimization by dynamically changing configuration parameters based on the computing environment's characteristics. The classifier model analyzes environment-specific execution metrics and adjusts configuration parameters accordingly, allowing seamless adaptation when applications are migrated between different computing environments without increasing management complexity

Inventive Principle:

Principle #35Parameter changes

Solution Approach 2:

The classifier model serves multiple functions: it detects configuration inefficiencies, identifies optimal configurations, and adapts to different computing environments. This universal approach allows the same system to handle various deployment scenarios and environment types, maintaining deployment flexibility while simplifying configuration management across diverse environments

Inventive Principle:

Principle #6Universality (Multi-functionality)

3Reliability

If inefficient configurations are not detected, then system operation continues without interruption, but resource utilization worsens due to excessive consumption

Engineering Contradiction:

Improvesystem operation continuityVSAvoidresource utilization

Core Design Contradiction:

ReliabilityVSUse of energy by moving object

Solution Approach 1:

The system maintains continuous operation by implementing ongoing monitoring of application execution metrics through the classifier model. Rather than interrupting system operation for configuration checks, the system continuously analyzes metrics and automatically detects configuration inefficiencies in real-time, ensuring both operational continuity and efficient resource utilization

Inventive Principle:

Principle #20Continuity of useful action

Solution Approach 2:

The system replaces manual configuration monitoring and adjustment mechanisms with an automated classifier model that uses machine learning to detect configuration inefficiencies. This substitution eliminates the need for manual intervention while maintaining system operation continuity and significantly improving resource utilization through automated detection and correction

Inventive Principle:

Principle #28Mechanics substitution (Replace mechanical system)

Data Source

PatentUS11740988B2Secure detection and correction of inefficient application configurations

Publication Date: 2023.08.29 RED HAT LLC

AI summary

Methods and systems for detecting and correcting inefficient application configurations are presented. In one embodiment, a method is provided that includes receiving, at a first computing environment, a configuration for and execution metrics of an application executed within a second computing environment; identifying, via a machine learning model, a corresponding preferred configuration and expected metrics associated with the corresponding preferred configuration; identifying a difference between at least one of the execution metrics and at least one of the expected metrics; and applying the corresponding preferred configuration to the application within the second computing environment.