This is a summary based on independent GO-EUC research generated by Google NotebookLM. For full technical details, test data, and methodology, visit GO-EUC research.
Executive Summary
This research investigates the consistency and reliability of Azure deployment times when utilizing Infrastructure as Code (IaC) tools like Terraform. While IaC offers significant benefits in automating and standardizing IT infrastructure, the study reveals that deployment times can vary due to factors largely outside user control, even with identical configurations and automation tooling. The research highlights the critical importance of consistent deployment times, particularly in disaster recovery scenarios. Key findings include variations in deployment and destruction times across different Virtual Machine SKUs, with some SKUs demonstrating greater consistency and reliability than others. The day of the week also appeared to influence overall deployment times, suggesting potential impacts from public cloud demand. Furthermore, the study identifies common failure modes during deployments and emphasizes the benefits of using private DevOps agents for improved reliability.
Main Themes & Key Findings
1. Importance of Consistent Deployment Times
- Disaster Recovery: The source emphasizes that "in the event of a disaster recovery, it is essential to have consistent, dependable, and rapid deployments." This underscores the practical necessity of predictable deployment durations beyond mere automation convenience.
2. Infrastructure as Code (IaC) Principles and Benefits
- Definition: IaC is defined as "a method of automating and managing IT infrastructure using code rather than manually setting it up through a user interface."
- Primary Benefit: The core advantage of IaC is its ability to "produce a consistent and repeatable environment." By defining environments as configurations in code, organizations can ensure "consistently deployed and maintained" resources, leading to "reduced errors and enable faster and more reliable deployments."
- Declarative vs. Imperative: The research highlights a shift from imperative (step-by-step logic, e.g., PowerShell) to declarative (desired state, e.g., Terraform) approaches. Terraform exemplifies the declarative way, where users "describe the desired state of the resource," and the tool handles the logic, leveraging a "State" file to track resources and changes. This approach is "very efficient and effective way to provision infrastructure."
3. Research Methodology
- Tooling: A "standard and straightforward Terraform deployment configuration" was used, stored in a Git repository in Azure DevOps.
- Components: The Terraform configuration included common Azure components such as Resource Group, Virtual Network, Subnet, Network Security Group, Windows Virtual Machine, and Public IP, among others.
- VM SKUs Tested: Four different VM SKUs were used as variables: Standard_B2s, Standard_D2s_v5, Standard_F4s_v2, and Standard_E4s_v5.
- Automation: An Azure DevOps pipeline, consisting of Terraform build and destroy stages, was scheduled to run every 30 minutes. Stages for each SKU were executed in parallel.
- Execution Environment: Deployments ran on "private DevOps agents that are hosted in a Docker container on a dedicated machine."
- Data Collection: Timing data (start and completion times) from pipeline runs and stages was collected via the Azure DevOps API, with failures marked accordingly.
- Volume: Each deployment ran a minimum of 10 times over several days.
4. Deployment Time Results and Variability
- Deployment vs. Destruction: As expected, "creating the infrastructure takes more time than removing it." On average, it took approximately 7.5 minutes to deploy all components.
- SKU-Dependent Variability:Deployment times "vary between the SKUs, which can be almost 2 minutes," influenced by the underlying hardware resources of the SKU (e.g., faster CPU improving times).
- Standard_E4s_v5: Identified as "the most consistent and fastest average deployment times," making it a "strong candidate for use in scenarios that require reliable provisioning, such as disaster recovery."
- Standard_B2s: Showed "the opposite, with higher deployment times and the most failures." Its "inconsistency makes it less suitable for production or time-sensitive workloads, despite its lower cost."
- Standard_D2s_v5 and Standard_F4s_v2: Provided a "good middle ground, delivering consistent and predictable deployment times."
- Day-of-Week Influence: "There is a consistent drop-off in the time it takes to run over the last couple of days." While the dataset was too small for definitive conclusions, the author suggests this "might be due to the day of the week" and demand in the public cloud.
5. Deployment Failures and Causes
- Observed Failures: 121 failures were recorded during infrastructure deployment.
- Primary Cause: VM OS Provisioning Timeouts: The leading error was "OS Provisioning for VM 'go-vm-1' did not finish in the allotted time." These timeouts led to "incomplete state files" and subsequent pipeline failures.
- SKU Reliability Implication: "An interesting observation is that it occurs one time for the Standard_F4s_v2 and the rest for the Standard_B2s. This would suggest that some SKUs are less reliable than the others." This reinforces the finding that Standard_B2s is less suitable for time-sensitive workloads.
- Secondary Cause: Terraform Provider Download Errors: Another error noted was "Error while installing hashicorp/azurerm v4.37.0: read tcp 0.0.0.0:45376->0.0.0.0:443: read: connection reset by peer." This points to potential "timeout[s] during downloading" due to reliance on internet connectivity.
6. Recommendations for Reliability
- Private DevOps Agents: The research "highly recommended to use a private DevOps agent when you require a reliable deployment time," as it "ensures you don’t have to wait for the public queue to clear and allows you to run the pipeline directly."
Conclusion
While Infrastructure as Code (IaC) significantly enhances the automation and repeatability of deployments, this research demonstrates that "deployment times are not always consistent," even with identical tools and configurations. Various external factors, often beyond user control (such as public cloud demand or underlying hardware performance for specific SKUs), can influence deployment duration and reliability. The study specifically highlights the variable performance of different Azure VM SKUs, with Standard_E4s_v5 proving most consistent and Standard_B2s being the least reliable. The findings underscore the need for thorough testing and selection of appropriate resources (like reliable SKUs and private DevOps agents) to achieve dependable deployment times, particularly in critical scenarios like disaster recovery. Further investigation into factors like the day-of-week influence on deployment times is suggested.
