Container Image Optimizer for Automated Resource Allocation

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Solution Overview

Problem

Manual coding of parameters for building disk images into containers often leads to inefficiencies, such as over- or under-allocation of resources like memory and disk space, and errors in identifying dependencies, resulting in wasted resources and errors in image creation.

Innovation Solution

A container image optimizer that acts as a digital twin of an existing container, using a generic data-driven machine-learning model to analyze the time evolution of system parameters and automatically generate optimal parameters for creating disk images, thereby eliminating the need for manual coding and improving resource allocation.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Ease of operation

If manual coding of parameters is used for building disk images into containers, then ease of operation is maintained, but resource allocation efficiency deteriorates due to over- or under-allocation of memory and disk space

Engineering Contradiction:

Improvemanual parameter codingVSAvoidresource allocation efficiency

Core Design Contradiction:

Ease of operationVSProductivity

Solution Approach 1:

The system performs self-service by automatically analyzing sensor data and generating optimized parameters for disk image creation without requiring manual intervention. The machine learning model autonomously determines memory and disk space allocations based on historical performance data, eliminating the need for manual parameter coding while improving resource allocation efficiency.

Inventive Principle:

Principle #25Self-service

Solution Approach 2:

The patent replaces the mechanical process of manual parameter coding with an automated machine learning system. The ML model processes sensor data and generates optimization parameters automatically, substituting human manual operations with an intelligent automated system that improves both ease of operation and resource allocation efficiency.

Inventive Principle:

Principle #28Mechanics substitution (Replace mechanical system)

2Device complexity

If manual parameter allocation is used, then device complexity is reduced, but manufacturing precision deteriorates due to errors in identifying dependencies and allocating resources

Engineering Contradiction:

Improveparameter setting complexityVSAvoidparameter accuracy

Core Design Contradiction:

Device complexityVSManufacturing precision

Solution Approach 1:

The system implements feedback by continuously collecting sensor data from container operations and using this information to refine parameter allocations. The machine learning model analyzes historical performance data and adjusts memory and disk space allocations based on actual usage patterns, improving parameter accuracy while maintaining simple operation through automated feedback loops.

Inventive Principle:

Principle #23Feedback

Solution Approach 2:

The patent applies preliminary action by pre-analyzing sensor data and generating optimized parameters before disk image creation. The system performs dependency analysis and resource allocation optimization in advance, ensuring accurate parameters are ready before the actual image building process, thereby improving manufacturing precision without increasing device complexity.

Inventive Principle:

Principle #10Preliminary action

3Productivity

If automated machine learning optimization is implemented, then resource allocation efficiency improves, but device complexity increases due to the need for ML models and sensor data processing

Engineering Contradiction:

Improveresource allocation efficiencyVSAvoidsystem architecture complexity

Core Design Contradiction:

ProductivityVSDevice complexity

Solution Approach 1:

The system achieves universality by designing a multi-functional platform that combines sensor data collection, machine learning analysis, and parameter generation in a single integrated system. The ML model serves multiple purposes including dependency analysis, resource allocation optimization, and performance prediction, reducing overall system complexity while improving resource allocation efficiency through consolidated functionality.

Inventive Principle:

Principle #6Universality (Multi-functionality)

Data Source

PatentUS11256493B1Container image optimizer

Publication Date: 2022.02.22 BANK OF AMERICA CORP

AI summary

A system accesses a disk image of a first software container and collected sensor data for a computer server. The system sequentially analyzes the sequence of layers of the disk image, and generates, based on the sequential analysis of the sequence of layers of the disk image, an auto coding sequence. The auto coding sequence includes a sequence of instructions for creating a new disk image. The system determines, based on the collected sensor data and the sequential analysis of the sequence of layers of the disk image, a sequential list of software needed for the computer server. The system determines, using the sequential list of software needed for the computer server, a plurality of infra requirements for a new software container. The system generates the new software container and the new disk image using the auto coding sequence and the plurality of infra requirements.