Pull-Speed Feedback for Automatic Container Image Registry Selection

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Solution Overview

Problem

Container orchestration platforms face challenges in efficiently selecting the fastest performing container image registry for clusters of host nodes due to varying performance issues across different geographic regions and network environments, leading to prolonged image pulling times and potential service disruptions.

Innovation Solution

A computer-implemented method that utilizes a status prediction module to collect historical performance data, generate a sorted list of container image registries based on performance, and an image optimization module to dynamically select and switch to the top performing registries for container image deployment, optimizing pulling speeds and ensuring high availability.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Speed

If container orchestration platforms use traditional single registry selection, then device complexity is low, but image pulling speed and service availability deteriorate due to slow or unavailable registries in varying geographic regions

Engineering Contradiction:

Improveimage pulling speedVSAvoidregistry selection complexity

Core Design Contradiction:

SpeedVSDevice complexity

Solution Approach 1:

The system performs preliminary actions by pre-configuring multiple container image registries with different geographic locations and pre-establishing performance monitoring mechanisms. Before actual image pulling occurs, the system has already prepared a sorted list of registries based on historical performance data, enabling fast selection during deployment without real-time complex calculations.

Inventive Principle:

Principle #10Preliminary action

Solution Approach 2:

The system implements feedback mechanisms by continuously monitoring image pulling performance from multiple registries and using this data to update the sorted list. The feedback loop collects pulling speed metrics, compares them against performance thresholds, and adjusts the registry selection accordingly, ensuring optimal performance while maintaining manageable complexity through automated learning.

Inventive Principle:

Principle #23Feedback

2Reliability

If the system monitors and records real-time pulling speeds from multiple registries, then service availability and reliability improve, but loss of time and computational resources increase

Engineering Contradiction:

Improveservice availabilityVSAvoidtime for performance monitoring

Core Design Contradiction:

ReliabilityVSLoss of time

Solution Approach 1:

The system applies partial action by monitoring and recording pulling speeds only from a selected subset of top-performing registries rather than all possible registries. The status prediction module identifies and focuses on the most relevant registries based on historical data, reducing the scope of monitoring to what is actually necessary for maintaining high service availability.

Inventive Principle:

Principle #16Partial or excessive action

Solution Approach 2:

The system uses preliminary action by pre-calculating and storing performance thresholds and historical data before actual deployment. The sorted list of registries is prepared in advance based on past performance, so during runtime the system only needs to compare current pulling speeds against pre-established criteria rather than performing complex real-time analysis.

Inventive Principle:

Principle #10Preliminary action

3Productivity

If the system automatically selects top performing registries based on historical data, then productivity of container deployment improves, but measurement precision requirements increase for accurate performance tracking

Engineering Contradiction:

Improvecontainer deployment efficiencyVSAvoidperformance data accuracy

Core Design Contradiction:

ProductivityVSMeasurement precision

Solution Approach 1:

The system applies parameter changes by adjusting the thresholds and weights used in performance measurement based on environmental conditions. The status prediction module can modify measurement criteria dynamically, such as changing the minimum pulling speed threshold or adjusting the importance of different performance metrics based on current network conditions and registry performance patterns.

Inventive Principle:

Principle #35Parameter changes

Solution Approach 2:

The system uses feedback to continuously refine measurement precision by comparing actual pulling performance against predicted performance and adjusting measurement parameters accordingly. The feedback mechanism allows the system to learn from deviations and improve its measurement accuracy over time without requiring perfectly precise initial measurements.

Inventive Principle:

Principle #23Feedback

Data Source

PatentUS12450047B2Automatic container image registry selection

Publication Date: 2025.10.21 INTERNATIONAL BUSINESS MACHINE CORPORATION

AI summary

Automatic container image registry selection is provided. A predefined number of top performing container image registries is selected from a top of a sorted list of a plurality of container image registries listed in descending order. A cluster of host nodes where a container is deployed is enabled to pull a same container image corresponding to the container utilizing different predefined pulling percentages from the predefined number of top performing container image registries. The cluster of host nodes utilizes a different predefined pulling percentage to pull the same container image from each respective top performing container image registry. A current real time average pulling speed of the cluster of host nodes for the same container image from each respective top performing container image registry of the predefined number of top performing container image registries is recorded over a defined time period.