Visual Clustering of Virtual Machine Images for Software Standardization
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Solution Overview
Problem
In cloud computing environments, managing and standardizing software stacks across multiple servers with different versions and customizations is labor-intensive and error-prone, as existing tools focus on individual machines rather than providing operations for image transformations and similarity analysis across environments.
Innovation Solution
A graphical tool that performs automatic clustering analysis, visualizing data to suggest standard images by mapping images and software components, allowing users to interact and modify clusters based on software compatibility and merging heuristics, and compressing data to identify 'golden masters' for consolidating functionality.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If existing inventory tools are used to analyze software components across multiple servers, then individual machine inventories can be obtained, but it becomes labor-intensive and error-prone to manually analyze and cluster images across the entire environment
Solution Approach 1:
The patent combines multiple individual machine inventories into a unified environmental view by clustering images based on software component similarities. The system merges data from numerous servers and virtual machines into consolidated clusters, allowing operators to analyze entire environments rather than individual machines, thereby reducing manual effort and analysis time while maintaining inventory accuracy.
Solution Approach 2:
The patent creates visual representations and models of the computing environment that copy and represent the actual software configurations. By generating visual clusters and similarity representations, the system allows operators to interact with simplified models rather than raw data, significantly reducing the time required to analyze and understand software inventories across the environment.
2Stability of the object's composition
If operators manually analyze and cluster images to standardize software stacks, then software standardization can be achieved, but the process is labor-intensive and error-prone
Solution Approach 1:
The patent enables the system to automatically perform clustering and identification of golden master images based on software component similarities. The system self-organizes the image inventory into clusters and identifies standardization candidates without requiring manual operator intervention for each clustering decision, thereby achieving software standardization while maintaining operational simplicity through automated processes.
Solution Approach 2:
The patent replaces manual mechanical analysis processes with automated computational clustering algorithms. Instead of operators manually comparing software components across images, the system uses automated similarity calculations and visual clustering techniques, substituting manual mechanical work with computational processes that are both more accurate and easier to operate.
3Loss of information
If detailed inventories of all software components are maintained for each image, then complete tracking information is available, but the complexity of analyzing and comparing across hundreds or thousands of images increases significantly
Solution Approach 1:
The patent segments the large set of images into smaller, manageable clusters based on software component similarities. By dividing the overall inventory into distinct clusters with shared characteristics, the system maintains complete tracking information for all software components while reducing the complexity of analysis through organized groupings that can be examined individually rather than as one large set.
Solution Approach 2:
The patent introduces visual dimensions and spatial representations to organize image data. By mapping images into visual clusters and using graphical interfaces to represent software component relationships, the system transforms complex tabular data into visual forms that are easier to analyze, maintaining complete information while reducing perceived complexity through dimensional transformation.
Data Source
AI summary
System, method and computer program product for generating a GUI that facilitates the management of real and/or virtual images on computing machines in a computing environment. The system and method provides for an interactive visualization of virtual images (machines) and the software components included in each virtual image or real image. According to a consolidating and clustering processes, the images are bundled and displayed in a dendogram to show a hierarchy of the similarity between images. Further, software components are represented by small coded cells and organized into logical groupings. The system and method provides for user interactive functionality that facilitates the gathering of details on certain aspects of the images and/or components. The end result is a software program that facilitates user's ability to consolidate and manage real and virtual images.