Dynamic Cloud Stack Configuration via Historic Analysis

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Solution Overview

Problem

Current cloud stack configuration techniques are limited, leading to network communication errors and performance degradation, as they fail to optimize cloud stacks for user needs and do not dynamically adapt to changes in cloud network loads or resource availability.

Innovation Solution

A dynamic cloud stack configuration system that includes a cloud network with a cloud stack server equipped with a configuration engine, which analyzes historic configurations to determine optimal component combinations and implements them, also featuring a testing engine to validate configurations and a tuning engine to adjust for performance issues.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Reliability

If current cloud stack configuration techniques are used, then cloud stacks can be configured, but network communication errors and performance degradation occur due to lack of optimization

Engineering Contradiction:

Improvenetwork communication reliabilityVSAvoidcloud stack performance

Core Design Contradiction:

ReliabilityVSProductivity

Solution Approach 1:

The system performs preliminary actions by analyzing historic cloud stack configurations before implementing new configurations. The configuration engine examines past successful configurations to predict and prevent potential network communication errors and performance degradation, ensuring optimized cloud stack setups are deployed proactively rather than reactively.

Inventive Principle:

Principle #10Preliminary action

Solution Approach 2:

The system implements feedback mechanisms by continuously monitoring cloud stack performance and comparing it against historic configurations. When performance degradation or network communication errors are detected, the system uses this feedback to adjust and optimize configurations dynamically, ensuring maintained reliability and productivity.

Inventive Principle:

Principle #23Feedback

2Productivity

If cloud stack configurations are optimized for user needs, then performance improves, but system complexity increases due to dynamic adaptation requirements

Engineering Contradiction:

Improvecloud stack performanceVSAvoidconfiguration system complexity

Core Design Contradiction:

ProductivityVSDevice complexity

Solution Approach 1:

The configuration engine operates autonomously by self-analyzing historic configurations and self-determining optimal settings without requiring complex external intervention. The system serves itself by automatically adapting cloud stack configurations based on performance data and historic patterns, reducing the need for manual complexity while maintaining high productivity.

Inventive Principle:

Principle #25Self-service

Solution Approach 2:

The system optimizes cloud stack performance by dynamically changing configuration parameters based on analyzed historic data. Rather than redesigning the entire configuration system, the engine adjusts specific parameters such as resource allocation, networking settings, and component interactions to achieve optimal performance while managing complexity.

Inventive Principle:

Principle #35Parameter changes

3Reliability

If historic cloud stack configurations are analyzed, then optimal configurations can be determined, but computational resources are consumed

Engineering Contradiction:

Improveconfiguration accuracyVSAvoidcomputational resource usage

Core Design Contradiction:

ReliabilityVSUse of energy by moving object

Solution Approach 1:

The configuration engine extracts only the essential and relevant features from historic cloud stack configurations rather than analyzing complete configuration datasets. By identifying and focusing on key parameters that most impact performance and reliability, the system determines optimal configurations with reduced computational overhead and lower energy consumption.

Inventive Principle:

Principle #2Taking out (Extraction)

Solution Approach 2:

The system performs partial analysis of historic configurations by examining only the portions of configuration data that are most relevant to current performance needs. Rather than comprehensively analyzing every detail of historic configurations, the engine applies selective analysis to achieve sufficient configuration accuracy while minimizing computational resource usage.

Inventive Principle:

Principle #16Partial or excessive action

Data Source

PatentUS10768956B2Dynamic cloud stack testing

Publication Date: 2020.09.08 BANK OF AMERICA CORP

AI summary

A dynamic cloud stack testing system comprises a cloud network with cloud components and a cloud stack server coupled to the network. The server includes an interface, a memory, a cloud stack configuration engine, and a cloud stack testing engine. The interface receives a cloud stack request from a user device that includes functionality parameters. The memory stores historic cloud stack combinations. The cloud stack configuration engine identifies cloud components associated with the functionality parameters and determines a cloud stack configuration that incorporates them. The cloud stack testing engine determines a cloud stack configuration test. The cloud stack testing engine executes the test, and stores results and the associated cloud stack configuration in the memory.