AWS Compute Services & Use Cases
AWS offers a variety of compute services to meet different workload requirements, providing scalability, flexibility, and cost optimization. SecureCart’s e-commerce platform requires a combination of EC2 instances, containerized workloads, and serverless computing to optimize performance, reduce costs, and scale effectively.
✔ Why does SecureCart need AWS compute services?
Supports dynamic workloads, including traffic spikes and seasonal demand.
Enables cost-efficient, on-demand, and auto-scaling infrastructure.
Facilitates containerized workloads and event-driven applications.
Ensures high availability and performance with auto-scaling and load balancing.
🔹 Step 1: Overview of AWS Compute Services
✔ AWS provides multiple compute services optimized for different workloads:
AWS Compute Service
Best For
Key Features
SecureCart Use Case
Amazon EC2
General-purpose workloads with full OS control.
Choose from various instance types, auto-scale, optimize CPU/memory.
Hosts SecureCart’s web application backend.
Amazon EC2 Auto Scaling
Dynamically adjusts instance count.
Scale based on CPU, memory, or network traffic.
Automatically scales checkout services during flash sales.
AWS Lambda
Serverless compute for event-driven workloads.
Runs code without managing infrastructure, scales automatically.
Processes order confirmations and sends transactional emails.
AWS Fargate
Serverless compute for containerized workloads.
Runs containers without managing underlying servers.
Manages SecureCart’s search and cart microservices.
Amazon ECS (Elastic Container Service)
Orchestrates Docker containers.
Managed container service, integrates with EC2 and Fargate.
Deploys SecureCart’s microservices with high availability.
Amazon EKS (Elastic Kubernetes Service)
Runs Kubernetes workloads.
Fully managed Kubernetes clusters for large-scale apps.
Hosts SecureCart’s AI-driven recommendation engine.
AWS Batch
Runs batch processing jobs efficiently.
Dynamically provisions compute resources.
Processes nightly analytics on customer purchasing trends.
AWS Elastic Beanstalk
Simplified application deployment.
Automates provisioning, scaling, and monitoring.
Deploys SecureCart’s internal tools with minimal overhead.
AWS Outposts
Runs AWS infrastructure on-premises.
Hybrid cloud with AWS-managed hardware.
Extends SecureCart’s compute power to data centers for compliance.
✅ Best Practices: ✔ Use EC2 for traditional workloads requiring full OS control. ✔ Leverage Lambda for event-driven applications with minimal overhead. ✔ Use Fargate for managing containers without provisioning EC2 instances. ✔ Implement Auto Scaling to dynamically adjust compute capacity.
🔹 Step 2: Choosing Between EC2, Containers, and Serverless Compute
✔ SecureCart must select the right compute model based on performance, scalability, and cost.
Compute Model
Best For
Advantages
SecureCart Use Case
EC2 Instances
Long-running workloads requiring full OS control.
Fine-tuned performance, full control over instances.
Hosts SecureCart’s order processing and database servers.
Containers (ECS/EKS/Fargate)
Microservices and highly scalable workloads.
Optimized resource utilization, auto-scaling.
Deploys SecureCart’s product search and recommendation engine.
Serverless (Lambda)
Event-driven and short-lived tasks.
Fully managed, cost-efficient, automatic scaling.
Processes order confirmations and payment notifications.
✅ Best Practices: ✔ Use EC2 for persistent workloads that require deep customization. ✔ Run microservices in containers for efficient resource utilization. ✔ Use Lambda for lightweight, asynchronous workloads.
🔹 Step 3: Scaling Compute with Auto Scaling & Load Balancing
✔ Why? – SecureCart must ensure high availability and responsiveness during peak traffic loads.
✔ AWS Scaling Strategies:
Scaling Approach
Purpose
SecureCart Implementation
Amazon EC2 Auto Scaling
Automatically scales instances based on demand.
Expands checkout servers during flash sales.
AWS Lambda Auto Scaling
Adjusts execution environment dynamically.
Handles sudden spikes in order processing.
ECS & EKS Auto Scaling
Adjusts container workloads dynamically.
Scales SecureCart’s product search services.
✔ AWS Load Balancing Strategies:
Load Balancer Type
Purpose
SecureCart Implementation
Application Load Balancer (ALB)
Routes HTTP/S traffic to microservices.
Balances traffic between checkout and catalog services.
Network Load Balancer (NLB)
Handles TCP/UDP traffic with low latency.
Optimizes SecureCart’s real-time inventory updates.
✅ Best Practices: ✔ Use ALB for web applications and microservices. ✔ Enable Auto Scaling to dynamically add/remove instances based on demand. ✔ Leverage NLB for high-throughput, latency-sensitive applications.
🔹 Step 4: Cost-Optimization Strategies for Compute
✔ Why? – SecureCart optimizes compute costs by choosing the right pricing model and instance types.
✔ AWS Compute Cost-Optimization Strategies:
Optimization Method
Purpose
SecureCart Implementation
Spot Instances
Low-cost compute for non-critical workloads.
Processes customer behavior analytics.
Savings Plans
Commit to usage for cost savings.
Optimizes SecureCart’s persistent EC2 workloads.
AWS Graviton-based Instances
ARM-based instances with better performance per dollar.
Runs SecureCart’s web application backend.
✅ Best Practices: ✔ Use Spot Instances for batch processing and fault-tolerant applications. ✔ Leverage Savings Plans for predictable compute workloads. ✔ Deploy Graviton-based instances (M7g, C7g) for better price-performance ratio.
🔹 Step 5: Monitoring & Performance Tuning
✔ Why? – SecureCart tracks compute performance to ensure high availability and efficiency.
✔ AWS Monitoring Tools for Compute Optimization:
Monitoring Tool
Purpose
SecureCart Use Case
Amazon CloudWatch
Tracks CPU, memory, and network usage.
Monitors EC2 and container performance.
AWS Compute Optimizer
Recommends right-sized instance types.
Suggests switching from M5 to M6g for better performance.
AWS X-Ray
Traces request latency across microservices.
Analyzes slow API responses in SecureCart’s checkout system.
✅ Best Practices: ✔ Enable CloudWatch Alarms for high CPU/memory usage. ✔ Use Compute Optimizer to right-size instances for cost efficiency. ✔ Monitor Lambda execution times to optimize performance.
🚀 Summary
✔ Use EC2 for full OS control, containers for scalable microservices, and Lambda for event-driven workloads. ✔ Implement Auto Scaling to dynamically adjust compute resources. ✔ Optimize costs using Spot Instances, Savings Plans, and Graviton-based instances. ✔ Leverage AWS monitoring tools (CloudWatch, X-Ray, Compute Optimizer) for performance tuning.
Scenario:
SecureCart must choose the best AWS compute service for different workloads, ensuring performance, scalability, and cost-efficiency.
Key Learning Objectives:
✅ Understand EC2, AWS Fargate, Lambda, and AWS Batch ✅ Learn when to use on-demand vs. reserved vs. spot instances ✅ Explore compute-intensive workloads with Amazon EMR & AWS Batch
Hands-on Labs:
1️⃣ Deploy an EC2 Instance & Benchmark Performance 2️⃣ Run a Batch Processing Job Using AWS Batch 3️⃣ Compare AWS Lambda & AWS Fargate for Event-Driven Workloads
🔹 Outcome: SecureCart selects the best AWS compute options for each business requirement.
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