Automated Capacity Optimization for Cloud Plug-in Components

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Solution Overview

Problem

Manual configurations in cloud environments face challenges in accurately determining and adjusting hardware and software requirements over time, leading to inefficiencies in resource management and scalability.

Innovation Solution

An automated system that retrieves metrics from plug-in components, cross-references them with sizing guidelines, generates software code modules, and determines the necessary software and hardware requirements for target computing components, enabling optimized operational functionality.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Measurement precision

If manual configurations are used to determine hardware and software requirements, then flexibility and control are maintained, but accuracy and efficiency deteriorate

Engineering Contradiction:

Improveaccuracy of hardware and software requirements determinationVSAvoidtime for manual configuration and adjustment

Core Design Contradiction:

Measurement precisionVSLoss of time

Solution Approach 1:

The system automatically retrieves metrics from plug-in components, cross-references them with sizing guidelines, generates software code modules, and determines hardware and software requirements without manual intervention. The automated system serves itself by continuously monitoring and adjusting resource allocation based on actual usage patterns

Inventive Principle:

Principle #25Self-service

Solution Approach 2:

The patent replaces manual mechanical configuration processes with automated electronic systems. The automated system uses electronic retrieval of metrics, computational cross-referencing with sizing guidelines, and automated generation of configuration code, substituting human manual operations with electronic automation

Inventive Principle:

Principle #28Mechanics substitution (Replace mechanical system)

2Adaptability or versatility

If manual configurations are used for cloud environments, then initial setup is possible, but continuous adaptation to changing needs deteriorates

Engineering Contradiction:

Improveability to adjust to changing resource needsVSAvoidoperational complexity of manual adjustments

Core Design Contradiction:

Adaptability or versatilityVSEase of operation

Solution Approach 1:

The system continuously monitors metrics from plug-in components and uses this feedback to automatically adjust resource allocation. The automated system retrieves current usage metrics, compares them against sizing guidelines, and dynamically adjusts hardware and software configurations to match actual needs

Inventive Principle:

Principle #23Feedback

Solution Approach 2:

The patent transforms static manual configurations into dynamic automated adjustments. The system continuously adapts resource allocation based on real-time metrics and usage patterns, allowing the infrastructure to dynamically respond to changing demands without manual reconfiguration

Inventive Principle:

Principle #15Dynamics

3Productivity

If automated systems are implemented for system capacity optimization, then productivity and accuracy improve, but device complexity increases

Engineering Contradiction:

Improveefficiency of resource allocation and scalabilityVSAvoidcomplexity of automated system architecture

Core Design Contradiction:

ProductivityVSDevice complexity

Solution Approach 1:

The automated system performs multiple functions within a unified architecture: retrieving metrics from various plug-in components, cross-referencing with sizing guidelines, generating software code modules, determining hardware requirements, and enabling operational functionality. This multi-functional approach consolidates complexity into a single versatile system

Inventive Principle:

Principle #6Universality (Multi-functionality)

Solution Approach 2:

The patent introduces an automated system as an intermediary layer between plug-in components and infrastructure resources. This intermediary automatically manages the complexity of resource allocation by retrieving metrics from components, processing them through sizing guidelines, and translating results into concrete hardware and software configurations

Inventive Principle:

Principle #24Intermediary (Mediator)

Data Source

PatentUS11586422B2Automated system capacity optimization

Publication Date: 2023.02.21 INTERNATIONAL BUSINESS MACHINE CORPORATION

AI summary

A method, system, and computer program product for implementing automated system capacity optimization is provided. The method includes retrieving from plug-in components running on a plurality of hardware and software sources, metrics data associated with the plug-in components. The metrics data is cross-referenced with respect to operational sizing recommendations for each plug-in component based on aggregated disparate sizing guidelines and resulting software code modules are generated. Software and hardware requirements for enabling target computing components are determined based on results of executing the software code modules and operational functionality of the target computing components are enabled in accordance with the software and hardware requirements.