Compute platform selection is a critical decision when migrating and modernizing workloads on AWS. Solutions architects must evaluate various factors to choose the optimal compute service that aligns with business requirements, performance needs, and operational capabilities.
AWS offers several co…Compute platform selection is a critical decision when migrating and modernizing workloads on AWS. Solutions architects must evaluate various factors to choose the optimal compute service that aligns with business requirements, performance needs, and operational capabilities.
AWS offers several compute options:
**Amazon EC2** provides virtual servers with full control over the operating system and configuration. It suits lift-and-shift migrations where applications require specific OS configurations or legacy dependencies. EC2 offers various instance families optimized for compute, memory, storage, or GPU workloads.
**AWS Lambda** enables serverless computing where you pay only for execution time. It excels for event-driven architectures, microservices, and variable workloads. Lambda eliminates server management overhead and scales automatically.
**Amazon ECS and EKS** provide container orchestration for containerized applications. ECS offers AWS-native container management, while EKS provides managed Kubernetes for organizations with existing Kubernetes expertise or multi-cloud strategies.
**AWS Fargate** offers serverless containers, removing the need to provision and manage underlying infrastructure while running container workloads.
**Key selection criteria include:**
1. **Application architecture**: Monolithic applications may start with EC2, while microservices benefit from containers or serverless.
2. **Operational expertise**: Teams familiar with Kubernetes might prefer EKS, while those seeking simplified management may choose Lambda or Fargate.
3. **Cost optimization**: Variable workloads benefit from serverless pricing models, while steady-state workloads may be cost-effective on Reserved Instances.
4. **Performance requirements**: Applications needing specific hardware, GPUs, or bare metal access require EC2.
5. **Migration timeline**: Quick migrations often use EC2, while modernization efforts may refactor toward containers or serverless.
6. **Compliance and security**: Some regulations require dedicated hosts or specific isolation levels.
Successful platform selection balances current application needs with future modernization goals, enabling organizations to optimize costs, improve scalability, and reduce operational burden over time.
Compute Platform Selection for AWS Solutions Architect Professional
Why Compute Platform Selection is Important
Compute platform selection is a critical skill for AWS Solutions Architects because choosing the right compute service has a profound impact on application performance, cost efficiency, operational overhead, and scalability. Making the wrong choice can result in over-provisioning (wasting money), under-provisioning (poor performance), or architectural complexity that hinders agility. For migrations and modernization efforts, selecting the appropriate compute platform determines the success of your cloud transformation journey.
What is Compute Platform Selection?
Compute platform selection refers to the process of evaluating and choosing the most suitable AWS compute service for a given workload based on specific requirements. AWS offers multiple compute options:
Amazon EC2 - Virtual servers with full control over the operating system and configuration AWS Lambda - Serverless functions that run code in response to events Amazon ECS - Container orchestration service using Docker containers Amazon EKS - Managed Kubernetes service for container orchestration AWS Fargate - Serverless compute engine for containers AWS Elastic Beanstalk - Platform-as-a-Service for deploying applications AWS Outposts - AWS infrastructure on-premises AWS Wavelength - Ultra-low latency applications at the edge
How Compute Platform Selection Works
The selection process involves evaluating several key factors:
1. Workload Characteristics - Is the workload stateful or stateless? - What are the runtime requirements (seconds, minutes, hours)? - Does it require specific operating systems or custom configurations?
2. Operational Requirements - How much operational overhead can the team manage? - Is there existing expertise in Kubernetes or containers? - What level of control is needed over the infrastructure?
3. Scaling Patterns - Does the workload have predictable or unpredictable traffic? - Is there a need for rapid scaling to zero? - What are the peak and baseline resource requirements?
4. Cost Considerations - Pay-per-request (Lambda) vs. pay-per-hour (EC2) - Reserved capacity vs. on-demand pricing - Cost of operational overhead and management
5. Integration Requirements - What other AWS services need to integrate with the compute platform? - Are there specific networking requirements? - What data persistence patterns are needed?
Decision Framework
Choose EC2 when: You need full control, specific OS configurations, long-running processes, GPU instances, or lift-and-shift migrations with minimal changes.
Choose Lambda when: You have event-driven workloads, intermittent traffic, need to scale to zero, or want to minimize operational overhead for short-duration tasks.
Choose ECS/EKS when: You have containerized applications, need portability, want microservices architecture, or require orchestration capabilities.
Choose Fargate when: You want containers but prefer not managing the underlying infrastructure.
Choose Elastic Beanstalk when: You want quick deployment with managed infrastructure and less configuration complexity.
Exam Tips: Answering Questions on Compute Platform Selection
Tip 1: Look for Keywords - "Minimize operational overhead" or "reduce management" → Serverless (Lambda, Fargate) - "Full control" or "custom AMI" → EC2 - "Kubernetes" or "K8s expertise" → EKS - "Docker containers" → ECS or Fargate - "Event-driven" or "triggered by" → Lambda
Tip 2: Consider Time Constraints - Lambda has a 15-minute execution limit - Long-running batch jobs typically need EC2 or containers - Sporadic, short-duration tasks favor Lambda
Tip 3: Evaluate Scaling Requirements - "Scale to zero" suggests serverless options - "Predictable workloads" may benefit from Reserved Instances on EC2 - "Unpredictable spikes" favor auto-scaling with Lambda or Fargate
Tip 4: Assess Migration Context - "Lift-and-shift" typically means EC2 - "Modernization" suggests containers or serverless - "Minimal code changes" points toward EC2 or Elastic Beanstalk
Tip 5: Match Cost Models to Usage Patterns - Consistent 24/7 workloads → EC2 with Reserved Instances - Variable workloads with idle periods → Lambda or Fargate - Development/test environments → Spot Instances or Lambda
Tip 6: Consider State Management - Stateful applications typically need EC2 or containers with persistent storage - Stateless applications are ideal for Lambda and Fargate
Tip 7: Watch for Hybrid Scenarios - On-premises requirements → Outposts - Edge computing with mobile networks → Wavelength - Local Zones for latency-sensitive applications
Common Exam Traps to Avoid - Do not assume Lambda is always the answer for cost optimization - Remember that EKS requires Kubernetes expertise - Fargate removes infrastructure management but still requires container expertise - EC2 is not always wrong; sometimes it is the most practical choice for migrations