Azure Virtual Machines – Consider Upgrading G Series Machines to Ev5

The Ev5 series offers enhanced computational capabilities and more cost-effective pricing compared to older G series virtual machines. This policy recommends evaluating and migrating to newer machine types that provide better price-to-performance ratios.

Why This Policy Matters

Performance and Cost Efficiency

Upgrading to Ev5 series machines delivers multiple critical benefits:

• Enhanced Processor Technology: Newer Intel processors with improved architecture

• Better Performance-per-Dollar Ratio: More computational power at lower cost

• Reduced Total Cost of Ownership (TCO)

Improved Energy Efficiency

Concrete Cost Savings Example

Consider a direct comparison between older and newer machine types:

• Machine TypevCPUsRAMStorageMonthly Cost G4 Instance16224 GB3072 GB$3,212E32 v5 Instance32256 GB800 GB$1,472

• Key Insight: 54% cost reduction with a more performant machine configuration

Implementation Strategy

Infrastructure-as-Code Example (Terraform)

Before (Old Configuration)

resource "azurerm_virtual_machine" "example" {
  vm_size = "Standard_G4"
  # Other configuration parameters
}

resource "azurerm_virtual_machine" "example" {
  vm_size = "Standard_G4"
  # Other configuration parameters
}

resource "azurerm_virtual_machine" "example" {
  vm_size = "Standard_G4"
  # Other configuration parameters
}

After (Upgraded Configuration)

resource "azurerm_virtual_machine" "example" {
  vm_size = "Standard_E32_v5"
  # Other configuration parameters
}

resource "azurerm_virtual_machine" "example" {
  vm_size = "Standard_E32_v5"
  # Other configuration parameters
}

resource "azurerm_virtual_machine" "example" {
  vm_size = "Standard_E32_v5"
  # Other configuration parameters
}

Manual Migration Steps

Assessment

Inventory current G series machine deployments

Document current workload requirements

Analyze performance metrics

Planning

• Select appropriate Ev5 series replacement

• Create migration runbook

Schedule maintenance window

Migration

Backup existing systems

Provision new Ev5 instances

Migrate data and configurations

Validate performance

Best Practices

• Gradual Migration: Avoid simultaneous large-scale upgrades

• Performance Testing: Validate workload compatibility

• Cost Monitoring: Track actual savings post-migration

• Regular Review: Continuously assess machine type efficiency

Tools for Implementation

Infracost: Automatically detect and recommend VM series upgrades

Azure Cost Management

Azure Advisor

Real-World Scenarios

Web Application Hosting

A mid-sized digital agency migrated 20 G4 instances to Ev5, reducing monthly infrastructure costs by $32,400 while improving overall application responsiveness.

Data Processing Workloads

A big data analytics team replaced legacy G series machines with Ev5, achieving:

40% compute performance increase

55% cost reduction

Improved machine learning model training times

Potential Considerations

Potential Limitations

• Compatibility: Some legacy applications might require specific configurations

• Migration Complexity: Requires careful planning and testing

• Initial Migration Overhead: Short-term implementation costs

• When to Avoid Immediate Upgrade

Extremely customized legacy systems

Applications with very specific hardware dependencies

Imminent major system redesign

Frequently Asked Questions (FAQs)

How quickly can I realize cost savings?
Typically within 1-3 months after migration, depending on workload complexity.

Are there risks in migrating?
Minimal risks if proper planning and testing are conducted. Always maintain backups and have rollback strategies.

Does Infracost help with this migration?
Yes, Infracost’s free trial can help identify potential VM series upgrade opportunities during infrastructure planning.

What if my specific workload has unique requirements?
Conduct thorough performance testing and consult Azure documentation for precise compatibility assessment.

How often should I review machine series?
Recommended quarterly review of infrastructure to ensure ongoing optimization.