Container Image Layer Recovery Without Service Restart
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Solution Overview
Problem
Existing container technologies face inefficiencies in handling patches in error, requiring users to wait for fixes or redeploy lower versions, leading to downtime and loss of functionality, especially when higher versions with patches in error are deployed in production environments.
Innovation Solution
Introduce a diff-relocation mechanism with attributes 'diff-removal' and 'diff-rebase' to mask or rebase layers with patches in error without redeploying containers, allowing seamless recovery and rebasing of image layers with patches in error on local graphs, ensuring minimal disruption.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If container image is redeployed to fix patches in error, then system reliability is improved, but service downtime increases and productivity decreases
Solution Approach 1:
The container image is segmented into multiple layers, allowing individual layer recovery without redeploying the entire container. The system identifies and recovers only the specific image layer containing the patch in error, while preserving other functional layers, thus maintaining productivity while improving reliability.
Solution Approach 2:
The system performs preliminary identification of the erroneous patch location in the image layer before initiating recovery. By pre-locating the problematic layer and preparing recovery mechanisms in advance, the system minimizes service downtime and maintains productivity during the reliability restoration process.
2Reliability
If container is restarted to apply fixes, then system reliability is improved, but service continuity is disrupted and time is lost
Solution Approach 1:
The erroneous patch is extracted from the problematic image layer, allowing the system to remove only the defective component without restarting the entire container. This selective extraction enables reliability improvement while maintaining service continuity and minimizing time loss.
Solution Approach 2:
The system performs preliminary identification of the erroneous patch and prepares the recovery mechanism before service disruption occurs. By having the recovery process ready in advance, the system can quickly restore reliability with minimal service downtime.
3Reliability
If lower version is deployed to avoid patches in error, then system reliability is improved, but functionality is lost and adaptability decreases
Solution Approach 1:
The container image is segmented into layers, enabling the system to retain the higher version's functional layers while recovering only the specific layer with the patch in error. This segmentation allows the system to maintain both reliability and the adaptability/features of the higher version simultaneously.
Solution Approach 2:
The recovery process applies local quality improvement by fixing only the specific image layer containing the erroneous patch, while preserving the quality and functionality of other layers. This localized approach maintains system reliability without sacrificing the adaptability and features of the higher version.
Data Source
AI summary
Examples described herein provide a computer-implemented method that includes downloading a container image from an image repository. The method further includes deploying the container image as a container at a local graph. The method further includes identifying an image layer of the container image of the container as having a patch in error. The method further includes performing at least one of recovering and rebasing the image layer having the patch in error without redeploying the container image at the local graph or restarting a service running the container.