Container AI Component Priority Management via Remote Access

Resolve Bottlenecks,
Find Innovative SolutionsGenerate Solutions

Solution Overview

Problem

Container-based virtualization technologies face challenges in managing execution priorities of AI components, leading to potential system performance degradation and error occurrences, and have limitations in remotely calling internal container programs, especially when interacting with remote systems.

Innovation Solution

A method for managing execution priorities of AI components within containers using a Docker-based system, which includes creating and managing containers, installing AI components, and enabling remote access and management through remote access units and network abstraction modules to optimize performance and allow remote editing of AI components.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Adaptability or versatility

If AI components are installed in containers with standardized interfaces, then system functionality and adaptability are improved, but system performance degrades and error frequency increases due to differential processor assignment

Engineering Contradiction:

ImproveAI component interoperabilityVSAvoidsystem stability

Core Design Contradiction:

Adaptability or versatilityVSReliability

Solution Approach 1:

The patent implements dynamic execution priority adjustment for AI components within containers. The system allows runtime modification of processor assignment priorities, enabling flexible resource allocation that adapts to different operational conditions while maintaining system stability. This resolves the contradiction by making the resource allocation dynamic rather than fixed, allowing both high adaptability for different AI components and controlled stability through priority management.

Inventive Principle:

Principle #15Dynamics

Solution Approach 2:

The patent changes the execution priority parameter of AI components installed in containers. By adjusting this parameter, the system can optimize resource allocation for different AI workloads while maintaining overall system stability. This parameter change enables standardized AI component interfaces to function effectively without causing performance degradation or frequent errors.

Inventive Principle:

Principle #35Parameter changes

2Productivity

If container-based virtualization is used to isolate computing resources, then execution speed is enhanced and resource usage is minimized, but remote access to internal container programs becomes difficult

Engineering Contradiction:

Improveexecution speedVSAvoidremote access capability

Core Design Contradiction:

ProductivityVSEase of operation

Solution Approach 1:

The patent introduces a remote access unit as an intermediary component within the container architecture. This intermediary enables remote programs to access and interact with internal container programs while maintaining the isolation benefits of containerization. The remote access unit acts as a controlled interface that preserves execution speed and resource efficiency while adding the necessary remote access capability.

Inventive Principle:

Principle #24Intermediary (Mediator)

3Power

If differential processor assignment is implemented for internal functions, then function-specific optimization is achieved, but system-wide stability decreases when errors occur

Engineering Contradiction:

Improveprocessor allocation efficiencyVSAvoidsystem stability

Core Design Contradiction:

PowerVSStability of the object's composition

Solution Approach 1:

The patent implements dynamic priority adjustment for processor assignment to AI components. Instead of fixed differential assignment, the system can adaptively change execution priorities based on operational conditions, allowing optimized resource allocation while maintaining system stability through controlled management of processor resources.

Inventive Principle:

Principle #15Dynamics

Solution Approach 2:

The patent incorporates feedback mechanisms that monitor system performance and stability. When errors or performance issues are detected, the system can adjust processor assignment priorities accordingly, providing feedback-driven optimization that maintains both processing efficiency and system stability.

Inventive Principle:

Principle #23Feedback

Data Source

PatentUS11226838B2Container-based management method by changing intelligent container component execution priority using remote calls via remote access unit and remote network functon module

Publication Date: 2022.01.18 KOREA ELECTRONICS TECH INST

AI summary

A method for managing AI components installed in containers is provided. The container-based component management method creates a container, installs at least one selected from a plurality of components in the container, and manages the components installed in the container. Accordingly, the execution priorities of the AI components installed in the containers can be managed and operated, such that degradation of system performance and frequent error occurrence can be prevented.