Smart Cloud Deployment Engine Self-Calibration

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Solution Overview

Problem

Conventional methods for deploying cloud-based applications are tedious, time-consuming, prone to errors, and lack efficient error detection and remediation, leading to potential malicious corruption and deployment issues in complex environments.

Innovation Solution

A smart cloud deployment engine equipped with semi-supervised machine learning algorithms that integrates deployment procedures with remediation and audit processes, adjusts parameters based on past results, and uses a decision module to validate deployment guidelines, triggering remediation measures and providing data analytics for improved deployment accuracy.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Productivity

If conventional cloud deployment methods are used, then deployment can be performed, but the process is tedious, time-consuming and prone to errors

Engineering Contradiction:

Improvedeployment speedVSAvoiddeployment accuracy

Core Design Contradiction:

ProductivityVSReliability

Solution Approach 1:

The deployment engine performs self-calibration by automatically detecting deployment attributes, comparing them against defined guidelines, and remediating mismatches without human intervention. The system uses machine learning algorithms to learn from past deployment outcomes and automatically adjust deployment parameters, enabling the system to service itself and eliminate manual calibration steps.

Inventive Principle:

Principle #25Self-service

Solution Approach 2:

The system implements continuous feedback loops where deployment outcomes are monitored, analyzed, and fed back into the deployment engine. The engine uses this feedback to recalibrate deployment parameters and improve future deployments. Audit procedures provide additional feedback mechanisms to validate deployment guidelines and trigger remediation when deviations are detected.

Inventive Principle:

Principle #23Feedback

2Manufacturing precision

If manual calibration of deployment parameters is performed, then deployment accuracy may be improved, but the process becomes more time-consuming and repetitive

Engineering Contradiction:

Improvedeployment precisionVSAvoidcalibration time

Core Design Contradiction:

Manufacturing precisionVSLoss of time

Solution Approach 1:

Inventive Principle:

Principle #25Self-service

Solution Approach 2:

The system replaces manual mechanical calibration processes with automated electronic and software-based mechanisms. Machine learning algorithms and automated detection systems substitute for human operators, using computational methods to analyze deployment attributes and adjust parameters, thereby eliminating the time-consuming manual calibration process while maintaining or improving precision.

Inventive Principle:

Principle #28Mechanics substitution (Replace mechanical system)

3Productivity

If deployment attributes are not validated, then deployment can proceed quickly, but undetected mismatches create significant problems with deployed applications

Engineering Contradiction:

Improvedeployment speedVSAvoiddeployment errors

Core Design Contradiction:

ProductivityVSObject-affected harmful factors

Solution Approach 1:

The system performs preliminary validation of deployment attributes against defined guidelines before deployment execution. By proactively detecting and remediating mismatches in advance, the system prevents deployment errors from occurring in the first place. This preliminary anti-action approach eliminates harmful factors before they can affect the deployed application, rather than reacting to problems after deployment.

Inventive Principle:

Principle #9Preliminary anti-action

Solution Approach 2:

The system uses detected deployment mismatches and errors as learning opportunities to improve future deployments. By analyzing deployment outcomes and attribute mismatches, the system converts potentially harmful errors into beneficial feedback that refines the deployment engine's calibration and prevents similar errors in future deployments.

Inventive Principle:

Principle #22Blessing in disguise (Convert harm into benefit)

4Reliability

If comprehensive audit procedures are implemented, then deployment reliability is improved, but system complexity increases

Engineering Contradiction:

Improvedeployment reliabilityVSAvoidsystem complexity

Core Design Contradiction:

ReliabilityVSDevice complexity

Solution Approach 1:

The deployment engine performs multiple functions within a single integrated system: deployment execution, attribute detection, guideline validation, error remediation, and audit procedures. By consolidating these functions into one universal platform, the system achieves comprehensive audit capabilities without proportionally increasing overall system complexity, as shared components and machine learning algorithms serve multiple purposes.

Inventive Principle:

Principle #6Universality (Multi-functionality)

Data Source

PatentUS10896083B2Dynamic cloud deployment and calibration tool

Publication Date: 2021.01.19 BANK OF AMERICA CORP

AI summary

Systems, apparatus and methods for intelligent deployment(s) of application objects are provided. The systems, apparatus and methods may include one or more dynamic parameters retrieved from metadata table(s). The parameter(s) may be used to calibrate the deployment(s). The parameter(s) may be associated with previous failed deployment(s). Calibration may be automatic. Calibration may include email sending and/or email previewing components. A testing environment may be used prior to actual deployment.