Machine Learning VM Prioritization for Restoration Efficiency
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Solution Overview
Problem
Existing virtual machine restoration processes are inefficient due to the lack of effective prioritization and handling of critical and non-critical VMs, leading to suboptimal use of computing resources and potential disruptions during restoration.
Innovation Solution
A method using a trained machine learning model to prioritize critical VMs over non-critical ones, identifying and excluding ineligible VMs, and consolidating lists for efficient restoration order, thereby optimizing resource allocation and ensuring critical VMs are prioritized.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If virtual machines are restored in arbitrary order without prioritization, then restoration process is simple to implement, but restoration time is extended and critical VMs may not be restored successfully
Solution Approach 1:
The system performs preliminary classification of VMs into critical and non-critical groups before restoration begins. A trained machine learning model evaluates VM characteristics and assigns priority levels in advance, creating a restoration sequence that optimizes both speed and success probability without adding complex real-time decision-making during the restoration process itself
Solution Approach 2:
The restoration process is segmented into distinct phases: critical VM identification, priority assignment using ML model, and sequential restoration execution. This segmentation allows each phase to be optimized independently, with the ML model handling classification and the restoration engine handling execution, thereby improving overall productivity while managing complexity through modular design
2Productivity
If all virtual machines including ineligible ones are included in restoration, then restoration process is simple, but computing resources are wasted and restoration efficiency decreases
Solution Approach 1:
The system extracts and identifies ineligible VMs from the restoration queue using the machine learning model, which evaluates VM states and determines eligibility based on multiple criteria. These ineligible VMs are removed from the restoration process before execution begins, preventing waste of computing resources on impossible or unnecessary restorations while maintaining simple inclusion-based restoration for eligible VMs
Solution Approach 2:
The machine learning model autonomously evaluates each VM's eligibility and priority without requiring manual intervention or complex external verification processes. The system self-determines which VMs should be restored and in what order, improving efficiency by eliminating resource-wasting restorations while keeping the overall process automated and simple to operate
Data Source
AI summary
A method for performing a restoration operation includes obtaining a restoration request, wherein the restoration request specifies a virtual machine (VM) set to be restored, and in response to the restoration request: identifying a first set of virtual machines (VMs), performing a critical VM prioritization on the first set of VMs using 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 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 restoration of virtual machines using the final list.