Virtual Machine Backup Prioritization via Machine Learning
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Solution Overview
Problem
Existing backup systems face inefficiencies in prioritizing and managing large numbers of virtual machines during backup operations, leading to suboptimal use of computing resources and increased risk of critical VMs not being backed up in case of disruptions.
Innovation Solution
A method utilizing a machine learning algorithm and trained model to prioritize critical virtual machines over non-critical ones, identifying and excluding ineligible VMs, and ordering backups to ensure critical VMs are backed up first, thereby optimizing resource utilization and ensuring critical data integrity.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If backup operations are performed on all virtual machines without prioritization, then complete backup coverage is achieved, but backup time and computing resource consumption increase significantly
Solution Approach 1:
The patent segments the virtual machine backup process into distinct priority levels (critical, high, medium, low) based on machine learning analysis of VM characteristics. This segmentation allows the system to process VMs in prioritized batches rather than treating all VMs equally, thereby reducing overall backup time while ensuring critical VMs are backed up first with complete coverage maintained for essential systems.
Solution Approach 2:
The patent performs preliminary classification and prioritization of virtual machines before the actual backup operation begins. By using machine learning models to pre-analyze VM characteristics, workloads, and criticality levels, the system prepares an optimized backup sequence in advance, which significantly reduces the time required during the actual backup execution while maintaining complete coverage.
2Reliability
If all virtual machines are included in backup operations, then comprehensive data protection is ensured, but computing resource utilization becomes suboptimal
Solution Approach 1:
The patent applies local quality by assigning different backup priorities and resource allocation levels to different virtual machines based on their specific characteristics, workloads, and criticality. Instead of applying a uniform backup strategy to all VMs, the machine learning model analyzes individual VM properties and assigns appropriate backup parameters, thereby optimizing computing resource utilization while maintaining comprehensive data protection for all VMs.
Solution Approach 2:
The patent dynamically changes backup parameters such as priority level, backup frequency, and resource allocation based on machine learning analysis of VM characteristics. By adjusting these parameters according to actual VM needs rather than using fixed settings, the system achieves optimal computing resource efficiency while ensuring complete data protection across all virtual machines.
3Loss of time
If critical virtual machines are prioritized using machine learning, then backup time for critical VMs is reduced, but system complexity increases
Solution Approach 1:
The patent introduces a machine learning model as an intermediary component that bridges the gap between raw VM characteristics and backup priority decisions. This intermediary automatically analyzes VM properties, workloads, and criticality levels to generate prioritization recommendations, thereby reducing critical VM backup time without requiring complex manual configuration or decision-making processes in the backup system itself.
Solution Approach 2:
The patent enables the backup system to automatically prioritize critical virtual machines through machine learning-based analysis of VM characteristics and patterns. The system self-adjusts priority assignments based on learned patterns from historical data and current VM states, reducing critical VM backup time without increasing operational complexity for system administrators or requiring manual intervention.
Data Source
AI summary
A method for performing a backup operation includes obtaining a backup request, wherein the backup request specifies a virtual machine (VM) set, and, in response to the backup request: identifying a first set of virtual machines (VMs), wherein the first set of VMs comprises a first portion of the VM set, performing a criticality analysis on the first set of VMs using a machine learning algorithm and a trained machine learning model to obtain an ordered list of critical VMs, performing a non-critical VM prioritization on a second set of VMs using the machine learning algorithm and the trained machine learning model to obtain an ordered list of non-critical VMs, consolidating the ordered list of critical VMs and the ordered list of non-critical VMs to obtain a final list, and initiating a backup of virtual machines using the final list.