Virtual Machine Backup Grouping by Metadata
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Solution Overview
Problem
Existing systems face inefficiencies in backing up virtual machines due to varying computational resources and times required, leading to decreased performance and workload impact, as all virtual machines are typically backed up simultaneously regardless of their metadata differences.
Innovation Solution
The method involves grouping virtual machines based on metadata such as size, change information, and workload, and provisioning resources to minimize impact on workloads, allowing each group to be backed up independently with similar resources, thus optimizing backup generation times.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If all virtual machines are backed up simultaneously regardless of metadata differences, then backup completion is ensured, but computational resources are wasted and backup efficiency decreases
Solution Approach 1:
The patent segments virtual machines into different groups based on their metadata characteristics (size, change information, workload). Each group is then backed up using appropriately provisioned resources matched to its specific requirements, rather than treating all VMs uniformly. This segmentation allows efficient resource allocation where smaller groups use fewer resources while larger groups receive proportionally more resources.
2Loss of time
If computational resources are provisioned for all virtual machines simultaneously, then all backups can proceed in parallel, but the impact on virtual machine workloads increases
Solution Approach 1:
The patent applies local quality by provisioning computational resources according to the specific characteristics of each virtual machine group. Smaller groups with less data require fewer resources, while larger groups receive proportionally more resources. This localized resource allocation minimizes the overall impact on VM workloads while still achieving parallel backup processing across multiple groups.
3Productivity
If virtual machines are grouped by metadata similarities, then resource provisioning is optimized, but system complexity increases due to grouping logic
Solution Approach 1:
The patent changes the parameter of resource allocation from a uniform fixed approach to a dynamic parameter-based approach. Resources are provisioned based on metadata parameters such as virtual machine size, change information, and workload characteristics. This parameter-driven approach optimizes resource allocation efficiency while keeping the grouping logic manageable through defined metadata criteria.
Data Source
AI summary
Techniques described herein relate to a method for generating backups of virtual machines. The method may include, in response to identifying a backup generation event associated with virtual machines: obtaining, by a backup agent, virtual machine metadata associated with the virtual machines; identifying groups of virtual machines based on the virtual machine metadata; provisioning resources to generate backups of the virtual machines based on the groups of virtual machines; and generating a backup of the virtual machines based on the groups of virtual machines using the provisioned resources associated with the groups of virtual machines.