Automated VM to Container Conversion via Dependency Analysis
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Solution Overview
Problem
Converting services from hardware level virtualization platforms using virtual machines to operating system level virtualization platforms with containers is a manual, error-prone, and time-consuming process, requiring in-depth knowledge and relying heavily on IT administrators to identify and migrate processes.
Innovation Solution
An automated system that analyzes virtual machine configurations, identifies processes, and builds optimized container images by filtering out common operating system data and isolating service-specific data to determine dependencies and layering, reducing the need for manual intervention and improving resource efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If manual conversion process is used to migrate virtual machine services to containers, then administrative control and customization are improved, but time consumption and operational complexity increase
Solution Approach 1:
The system performs self-service by automatically analyzing virtual machine configurations, identifying processes and their dependencies, and generating container images without requiring manual administrative intervention. The automated analysis engine examines VM metadata, process relationships, and configuration files to autonomously complete the conversion process.
Solution Approach 2:
The manual mechanical process of administrative conversion is replaced with an automated computational system. The analysis engine uses computational methods to parse VM configurations, detect process links, and construct container images, substituting human administrative actions with automated software processes.
2Measurement precision
If manual conversion process is used to migrate virtual machine services to containers, then detailed configuration analysis is improved, but error rate and operational complexity increase
Solution Approach 1:
The system implements feedback mechanisms where the analysis engine continuously examines VM configurations, validates process dependencies, and adjusts its analysis based on detected relationships. The system verifies the accuracy of identified process links and configuration mappings, correcting errors through iterative validation against the actual VM state.
Solution Approach 2:
The analysis engine acts as an intermediary between the virtual machine environment and the containerization process. It mediates the conversion by objectively analyzing VM configurations and process relationships, translating VM metadata into container image specifications without human intervention, thereby reducing errors from manual interpretation.
3Reliability
If hardware level virtualization is used with virtual machines, then service isolation and management control are improved, but computational overhead and resource consumption increase
Solution Approach 1:
The patent extracts the essential service functionality from the virtual machine environment and packages it into container images. By taking out only the necessary application code and dependencies from the VM, the system eliminates the overhead of virtual machine monitoring and hardware emulation while preserving service isolation through container boundaries.
Solution Approach 2:
The system changes the virtualization parameter from hardware-level VM abstraction to operating-system-level container abstraction. This parameter change reduces computational overhead by eliminating the hypervisor layer and hardware emulation, while maintaining service isolation through OS-level container mechanisms.
4Reliability
If virtual machines with separate kernels are used for each service, then service independence and security are improved, but resource consumption and operational complexity increase
Solution Approach 1:
The patent merges multiple service processes into shared container images that run on a common operating system kernel. By combining services that share the same kernel requirements into single containers, the system reduces the quantity of kernels needed while maintaining service independence through container isolation mechanisms.
Solution Approach 2:
The system creates universal container images that can host multiple services sharing the same kernel. These multi-functional containers can accommodate different applications and processes that require the same OS kernel, eliminating the need for separate kernels for each service while preserving independence through container boundaries.
Data Source
AI summary
Technology for analyzing a target machine (e.g., virtual machine or physical machine) and converting the services of the target machine to one or more container images that can be run using operating system level virtualization. An example method may include: receiving, by a processing device, data of a virtual machine, the data indicating a configuration of the virtual machine and a set of processes executed by the virtual machine; identifying, by the processing device, computer code of a first process of the set of processes executed by the virtual machine; analyzing the computer code to detect a link between the first process and a second process of the set of processes; and building a container image in view of the data of the virtual machine and the identified link, wherein the container image comprises the computer code of the first process and computer code of the second process.