← Back
Carnegie Mellon University · 15-319

Cloud Computing

Six hands-on projects across AWS, GCP, and Azure — elasticity, containers, big data, storage, stream processing, and ML on the cloud.

AWS

Cloud Elasticity

LoadGeneratorElastic LoadBalancerAuto Scaling GroupEC2EC2EC2 ×n↕ scales on demandTerraform + Boto3Infrastructure as Code
  • Provisioned AWS Auto Scaling Groups and Elastic Load Balancers via Python Boto3 and Terraform, scaling EC2 instances to maintain 7–12 avg RPS with a hard cap of 35 RPS under variable load
  • Tuned scale-out and scale-in policies to operate within a 220–280 instance-hour budget, balancing throughput against cloud spend using CloudWatch metrics
AWSEC2Auto ScalingELBCloudWatchBoto3Terraform