Cloud Image Local Repository Segmentation
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Solution Overview
Problem
Deploying large cloud infrastructures faces challenges with central repositories being overwhelmed by network bandwidth and disk throughput issues when serving packages to numerous nodes, leading to inefficiencies in provisioning and configuration.
Innovation Solution
Generating cloud images with local repositories that store packages in an uninstalled state, allowing for efficient deployment and configuration of nodes by installing only the necessary packages on demand, reducing the need for re-imaging and minimizing data loss.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If a central repository is used to serve packages to numerous nodes, then package distribution is centralized and manageable, but network bandwidth and disk throughput are overwhelmed leading to provisioning inefficiencies
Solution Approach 1:
The patent segments the centralized repository into multiple distributed local repositories across different cloud images. Each cloud image contains a local repository with packages specific to its role, eliminating the single-point bottleneck. This segmentation allows parallel package installation across multiple nodes without competing for centralized resources, directly resolving the contradiction between centralized management and provisioning efficiency.
Solution Approach 2:
The patent performs preliminary action by pre-configuring cloud images with role-specific package sets stored in local repositories before deployment. When a node is provisioned, the required packages are already packaged and ready in the cloud image's local repository, enabling immediate installation without real-time retrieval from a central repository. This preliminary preparation eliminates provisioning delays while maintaining package management control.
2Speed
If all packages are pre-installed on cloud images, then nodes can be deployed quickly, but resource overhead increases and unnecessary packages consume storage and memory
Solution Approach 1:
The patent applies local quality by configuring each cloud image with a customized local repository containing only the package sets relevant to specific roles (e.g., web server, database, application server). Instead of including all possible packages in every cloud image, each image is tailored to its intended function. This reduces storage overhead and resource consumption while maintaining fast deployment, as each node receives only the packages it needs for its specific role.
Solution Approach 2:
The patent uses partial action by including only the necessary subset of packages in each cloud image's local repository rather than all available packages. Each cloud image contains package sets sufficient for its specific role but no more, avoiding the resource overhead of excessive packages. This selective inclusion maintains deployment speed while optimizing resource utilization.
3Adaptability or versatility
If cloud images are frequently updated with new packages, then nodes can be re-configured, but data loss occurs during re-imaging processes
Solution Approach 1:
The patent extracts the package management function from the cloud image itself, storing packages in a separate local repository within the cloud image structure. This separation allows the cloud image to remain intact while packages are independently updated, added, or removed in the repository. When configuration changes are needed, only the package repository is modified rather than re-imaging the entire cloud image, preserving existing data while providing configuration flexibility.
Solution Approach 2:
The patent introduces dynamics by making the package repository within each cloud image modifiable and adaptable without requiring cloud image reconstruction. The local repository can be dynamically updated with new packages or modified package sets to accommodate changing role requirements. This dynamic package management enables configuration flexibility while maintaining the stability of the underlying cloud image and preserving data integrity.
Data Source
AI summary
Examples disclosed herein relate to cloud images. The examples enable generating a first cloud image to configure a first node to serve a first role in a cloud infrastructure. The first cloud image comprising a first set of packages may be stored in a first local repository of the first cloud image. The examples further enable generating a second cloud image to configure a second node to serve a second role in the cloud infrastructure where the second cloud image comprises a second set of packages stored in a second local repository of the second cloud image. The examples further enable causing the first cloud image to be deployed to the first node in the cloud infrastructure and causing the second cloud image to be deployed to the second node in the cloud infrastructure.