Introduction to Azure Virtual Machines – It’s Types and Workloads

Why Azure Virtual Machine is Important?

Azure Virtual Machine (VM) holds significant importance for cloud computing and application development due to its versatile capabilities and benefits

• Flexibility: Azure VMs offer a wide range of sizes and configurations, catering to diverse workload requirements.
• Scalability: VMs can be quickly provisioned, scaled up or down based on demand, optimizing resource allocation.
• Cost Efficiency: Organizations avoid upfront hardware costs by using Azure VMs and paying only for the resources they consume.
• Global Reach: Azure’s extensive data centre network enables deployment across the globe, reducing latency for users.
• Dev/Test Environments: VMs facilitate creating isolated environments for testing and development, ensuring stability and innovation.
• Legacy Application Migration: Organizations can lift and shift existing on-premises applications to Azure VMs for improved accessibility and management.

Incorporating Azure VMs into cloud strategies enables businesses to efficiently deploy applications, optimize resource utilization, and achieve better flexibility and agility in their operations.

Types of Azure Virtual Machines

General-Purpose VMs:

• Balanced performance for a variety of workloads.
• Suitable for web servers, databases, and development environments.

Compute-Optimized VMs:

• High CPU-to-memory ratio for compute-intensive tasks.
• Ideal for batch processing and gaming servers.

Memory-Optimized VMs:

• Large memory sizes for memory-intensive applications.
• Suitable for in-memory databases and analytics.

Storage-Optimized VMs:

• High disk throughput and I/O capabilities.
• designed for the storage and processing of large amounts of data.

GPU VMs:

• Graphics processing unit acceleration for AI and machine learning.
• Used in scientific simulations and visualization.

High-Performance Compute VMs:

• Highest CPU and memory performance.
• Suitable for demanding high-performance computing (HPC) workloads.

Azure Spot VMs:

• Leverage unused Azure capacity at lower costs.
• Suitable for fault-tolerant applications and batch processing.

How can an Azure virtual machine be created?

Sign In to Azure Portal:

• Utilize your Azure account credentials to log into the Azure portal.

Navigate to Create a Virtual Machine:

• In the left-hand menu, select “Create a resource”.
• Select “Virtual Machine” after conducting a search for it.

Basics:

• Choose your subscription.
• Select an existing resource group or form one from scratch.
• Enter a VM name and choose a region.
• Select your desired availability options, such as Availability Set or Availability Zone.
• Select an image: Windows, Linux, etc.
• Choose an authentication type (password or SSH public key).

Instance Details:

• Choose the VM size based on your workload requirements.
• Specify the number of instances (if using VM Scale Sets).

Disks:

• Configure the OS disk size and type (Standard HDD/SSD or Premium SSD).
• Optionally, add data disks and configure settings.

Networking:

• Configure networking settings:
• Virtual networks: Pick one that already exists or construct one from scratch.
• Subnet: Choose a subnet within the virtual network.
• Public IP: Assign a public IP (if needed).
• Network security group: Configure network rules.
• NIC: Choose an existing network interface or create a new one.

Management:

• Configure auto-shutdown (optional).
• Enable Boot diagnostics for troubleshooting (optional).
• Choose whether to use a custom script extension.

Advanced:

• Configure extensions for post-deployment tasks (optional).

Tags:

• Add tags for better resource management and organization (optional).

Review + Create:

• Review your settings.
• Click “Create” to begin the VM deployment process.

Deployment Progress:

• The deployment progress will be displayed. Hold off until the deployment is finished.

Access the Virtual Machine:

• Once the VM is created, you can access it using remote desktop (RDP) for Windows or SSH for Linux.
• Use the credentials you provided during setup to log in.

Workloads for Azure Virtual Machine

Suitable workloads For Azure Virtual Machine:

• Web Applications: Hosting websites, blogs, e-commerce platforms, and content management systems.
• Development and Testing: Creating isolated environments for software development, testing, and debugging.
• Databases: Running databases like SQL Server, MySQL, PostgreSQL, or NoSQL databases to store and manage data.
• Application Hosting: Deploying and running various applications, both commercial and custom-built.
• File Servers: Storing and sharing files within an organization or for collaboration purposes.
• Business Applications: Running enterprise applications such as ERP, CRM, and business intelligence tools.
• Media and Content Delivery: Hosting media files, streaming videos, and delivering content to users.
• Virtual Desktops: Providing remote access to desktop environments for remote work or educational purposes.
• Data Processing and Analytics: Performing data processing, analysis, and reporting for business insights.
• Machine Learning and AI: Training machine learning models, running AI algorithms, and data analytics.
• Internet of Things (IoT): Managing and processing data from IoT devices and sensors.
• High-Performance Computing: Running complex simulations, scientific calculations, and data modelling.
• Backup and Disaster Recovery: Creating backup instances and recovery environments for business continuity.
• Educational Environments: Setting up virtual classrooms and learning management systems.
• Legacy Application Hosting: Running older applications that are not cloud-native.

Azure VMs offer the flexibility, scalability, and customizable configurations needed to accommodate these diverse workloads effectively.

Unsuitable workloads For Azure Virtual Machine:

While Azure Virtual Machines are versatile, there are certain workloads that might not be the best fit due to their specialized requirements or nature. Here are some examples of unsuitable workloads for Azure VMs

• Real-Time High-Frequency Trading: Workloads requiring ultra-low-latency and real-time processing.
• Massive Parallel Processing: Extremely large-scale data processing better suited for specialized clusters.
• Extreme Graphics Rendering: Workloads demanding high-end GPUs for advanced graphics rendering.
• Specialized High-Performance Computing (HPC): Extremely specific HPC applications needing dedicated hardware.
• Short-Lived Batch Jobs: Workloads with very short durations that might not justify VM provisioning time.
• Applications with Spiky Demand: Workloads with unpredictable usage spikes that might result in higher costs.
• Serverless Computing: For microservices and event-driven applications, consider Azure Functions.
• Long-Term Data Archiving: Consider Azure Storage solutions for cost-effective data archiving.
• Stateless Microservices: Consider Azure Kubernetes Service (AKS) for containerized microservices.
• Extremely Large Storage Needs: Azure VMs might not be the most cost-effective solution for massive data storage.

Remember that Azure offers specialized services to address many of these requirements. For workloads that might not align well with Azure VMs, explore other Azure services tailored to those specific needs.