Specify components of a compute solution based on workload requirements
5 minutes
5 Questions
When designing compute solutions in Azure, architects must carefully analyze workload requirements to select appropriate components. The process involves evaluating several key factors to ensure optimal performance, cost-efficiency, and scalability.
**Workload Analysis Considerations:**
1. **Proc…When designing compute solutions in Azure, architects must carefully analyze workload requirements to select appropriate components. The process involves evaluating several key factors to ensure optimal performance, cost-efficiency, and scalability.
**Workload Analysis Considerations:**
1. **Processing Requirements**: Determine if workloads are CPU-intensive, memory-intensive, or GPU-accelerated. Batch processing jobs may benefit from Azure Batch, while real-time applications might require Azure Functions or Container Instances.
2. **Scalability Needs**: Assess whether horizontal or vertical scaling is needed. Azure Virtual Machine Scale Sets provide automatic scaling for VM-based workloads, while Azure Kubernetes Service (AKS) offers container orchestration with built-in autoscaling.
3. **State Management**: Stateless workloads suit serverless options like Azure Functions, while stateful applications may require Virtual Machines or Azure Service Fabric.
4. **Availability Requirements**: High-availability workloads need Availability Zones, Availability Sets, or multi-region deployments. SLA requirements influence component selection.
5. **Compute Options Selection**:
- **Virtual Machines**: Full OS control, lift-and-shift scenarios
- **Azure App Service**: Web applications, APIs, mobile backends
- **Azure Functions**: Event-driven, short-duration tasks
- **Azure Container Instances**: Quick container deployment
- **Azure Kubernetes Service**: Complex microservices architectures
- **Azure Batch**: Large-scale parallel computing
6. **Performance Tiers**: Match VM sizes (B-series for burstable, D-series for general purpose, F-series for compute-optimized) to workload patterns.
7. **Cost Optimization**: Consider Reserved Instances for predictable workloads, Spot VMs for fault-tolerant batch processing, and consumption-based pricing for variable loads.
8. **Integration Requirements**: Evaluate how compute components connect with storage, networking, and other Azure services.
Architects should document workload characteristics including peak usage times, throughput requirements, latency sensitivity, and compliance constraints to make informed decisions about compute solution components.
Specify Components of a Compute Solution Based on Workload Requirements
Why This Topic Is Important
Understanding how to specify compute components based on workload requirements is fundamental to the AZ-305 exam and real-world Azure architecture. As a Solutions Architect, you must select the right compute services that align with performance needs, cost constraints, scalability requirements, and business objectives. Poor compute decisions lead to over-provisioning, under-performance, or excessive costs.
What Is Specifying Compute Components?
This involves analyzing workload characteristics and selecting appropriate Azure compute services such as:
• Azure Virtual Machines (VMs) - IaaS offering for full control over OS and applications • Azure App Service - PaaS for web applications and APIs • Azure Kubernetes Service (AKS) - Container orchestration platform • Azure Container Instances (ACI) - Serverless containers for simple scenarios • Azure Functions - Event-driven serverless compute • Azure Batch - Large-scale parallel and HPC workloads • Azure Virtual Desktop - Desktop virtualization service
How It Works
The selection process involves evaluating several key factors:
1. Workload Type Analysis • Web applications typically suit App Service or AKS • Batch processing aligns with Azure Batch or Functions • Legacy applications often require Virtual Machines • Microservices benefit from AKS or Container Apps
2. Scalability Requirements • Vertical scaling (scaling up) - Increasing VM size • Horizontal scaling (scaling out) - Adding more instances • Auto-scaling capabilities vary by service
3. Performance Considerations • CPU-intensive workloads may need compute-optimized VMs • Memory-intensive applications require memory-optimized VMs • GPU workloads need N-series or NC-series VMs
4. Availability Requirements • Availability Sets provide 99.95% SLA • Availability Zones provide 99.99% SLA • Zone-redundant services offer highest resilience
5. Cost Optimization • Reserved Instances for predictable workloads (up to 72% savings) • Spot VMs for interruptible workloads (up to 90% savings) • Right-sizing based on actual utilization
Decision Framework
When selecting compute services, consider this hierarchy:
1. Can it run serverless? → Azure Functions or Logic Apps 2. Is it containerized? → AKS, ACI, or Container Apps 3. Is it a web app? → Azure App Service 4. Does it need full OS control? → Azure Virtual Machines
Exam Tips: Answering Questions on Specifying Compute Components
• Read requirements carefully - Look for keywords like lift-and-shift (VMs), microservices (AKS), event-driven (Functions), or web application (App Service)
• Consider the least administrative effort - PaaS solutions like App Service reduce management overhead compared to IaaS
• Match SLA requirements - Questions mentioning 99.99% uptime point toward Availability Zones