Case Study of Infrastructure as a Service (IaaS) Using Amazon EC2

 Case Study of Infrastructure as a Service (IaaS) Using Amazon EC2

Objective

To illustrate the application of Amazon Elastic Compute Cloud (EC2) as an Infrastructure as a Service (IaaS) solution through a case study, highlighting its features, implementation, benefits, and challenges, with a focus on eliminating redundant points.

Case Study: Scaling GlobeMart Retail with Amazon EC2

Organization

GlobeMart Retail, a mid-sized e-commerce company, operates an online platform with a significant on-premises data center. The company serves a global customer base and experiences seasonal traffic spikes during events like Black Friday and Diwali.

Challenges

  • Performance Issues: On-premises servers struggled with traffic spikes, leading to slow performance and lost sales.
  • High Costs: Significant expenses for hardware maintenance, power, cooling, and data center space.
  • Slow Scalability: Provisioning new servers took weeks, hindering responsiveness to demand.
  • Disaster Recovery: Limited and costly disaster recovery (DR) capabilities.
  • Global Expansion: Setting up infrastructure in new regions was complex and time-consuming.


Amazon EC2 as IaaS

GlobeMart Retail migrated its e-commerce platform to Amazon EC2, leveraging AWS’s IaaS model to rent virtual servers (instances) while maintaining control over operating systems, middleware, and applications. EC2 was chosen for its scalability, pay-as-you-go pricing, and global infrastructure.

Implementation Process

  1. Requirement Analysis:
    • Identified needs for web servers, application servers, and self-managed databases.
    • Anticipated traffic patterns, especially during peak sales events.
  2. Instance Selection:
    • Deployed General Purpose (m5, m6g) instances for web and application tiers.
    • Used Memory Optimized (r5, r6g) instances with high-performance EBS volumes for databases (PostgreSQL).
  3. Deployment:
    • Created custom Amazon Machine Images (AMIs) with Linux and pre-configured software (e.g., Apache, Node.js) for rapid deployment.
    • Configured an Elastic Load Balancer (ELB) to distribute traffic across EC2 instances.
    • Set up Auto Scaling Groups to adjust instance counts based on CPU utilization (>70%) or network traffic.
  4. Storage:
    • Used Amazon Elastic Block Store (EBS) for persistent storage of databases and application data, with snapshots for backups.
    • Stored static assets (e.g., product images, CSS) in Amazon S3 for efficient content delivery.
  5. Networking and Security:
    • Deployed EC2 instances in an Amazon Virtual Private Cloud (VPC) with public and private subnets for isolation.
    • Configured Security Groups to allow HTTP (port 80), HTTPS (port 443), and SSH (port 22) traffic only.
    • Used AWS Identity and Access Management (IAM) for access control.
  6. Monitoring and Management:
    • Monitored performance with Amazon CloudWatch, setting alarms for CPU usage and latency.
    • Automated patching and updates using AWS Systems Manager.
    • Used AWS CloudFormation to provision infrastructure as code for consistency.
  7. Cost Optimization:
    • Combined On-Demand Instances for flexibility, Savings Plans for steady workloads, and Spot Instances for non-critical batch processing.
  8. Data Migration:
    • Migrated data using AWS DataSync and native database replication tools.
    • Conducted load testing to ensure performance before going live.

Dependencies

  • Database Dependencies:
    • Functional Dependencies: In the database (self-managed on EC2), ensured data integrity (e.g., OrderID → CustomerID, ProductID, OrderDate) for consistent order processing.
    • Inclusion Dependencies: Enforced referential integrity (e.g., Orders.CustomerID Customers.CustomerID) to prevent orphaned records.
  • Service-Level Dependencies:
    • EC2 relies on ELB for traffic distribution, EBS/S3 for storage, CloudWatch for monitoring, and VPC for network security, ensuring a cohesive system.

Results and Benefits

  • Scalability: Handled 500% traffic spikes during peak seasons without downtime, thanks to Auto Scaling and ELB.
  • Cost Efficiency: Reduced IT costs by 40% by eliminating data center expenses and using pay-as-you-go pricing.
  • Agility: Provisioned new servers in minutes, compared to weeks on-premises.
  • Reliability: Achieved high availability through multi-AZ deployments and robust DR using EBS snapshots and S3 in another region.
  • Global Reach: Launched in two new countries within three months by deploying EC2 instances in local AWS regions.
  • Control: Retained flexibility over OS and application configurations, easing migration of legacy systems.

Challenges

  • Complexity: Managing EC2, ELB, Auto Scaling, and VPC requires technical expertise.
  • Cost Management: Risk of cost escalation without proper monitoring of instance usage.
  • Vendor Lock-In: Heavy reliance on AWS services may limit future flexibility.
  • Learning Curve: Configuring AMIs, Security Groups, and scaling policies demands initial training.

Key EC2 Features

  • Scalability: Auto Scaling adjusts resources dynamically.
  • Flexibility: Multiple instance types (e.g., m5, r5) and OS options (Linux, Windows).
  • Cost-Effectiveness: Pay-as-you-go, Spot Instances, and Savings Plans.
  • High Availability: Multi-AZ deployments for fault tolerance.
  • Security: Security Groups, VPC, and IAM for robust access control.
  • Integration: Seamless connectivity with S3, EBS, CloudWatch, and ELB.

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