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This project, Energy-Efficient DevOps: Auto-Suspending Idle AWS Resources Using Machine Learning, aims to reduce unnecessary cloud costs
Week 1 – Setup Phase (Problems & Progress) Objective Set up EC2 instances with basic monitoring tools to simulate DevOps workloads and start collecting usage data for ML-based auto-suspension. Problem 1: SSH to EC2 Failing on Windows Error Message:...
Week 1 – Setup Phase (Problems & Progress) Objective To simulate periodic DevOps workloads on the EC2 instance using cron and stress commands, and begin logging CPU usage every minute to generate time-series data for future machine learning model tr...
Week 2 – ML Modeling Phase (Log Extraction & Model Training) Objective: To extract the logged CPU and workload data from the EC2 instance, preprocess it, label idle/active states, and train a machine learning model to detect idle periods. What I Did...
Week 2– Automation Phase (Auto-Suspend Script Deployment) Objective: To automate EC2 instance suspension when the machine learning model predicts an idle state, using boto3, instance metadata, and cron jobs. What I Did Today: Transferred Trained Mod...
Week 2 – Rebuilding Infra with Terraform + S3 Backups Objective: This week, I focused on phasing out the old manually created EC2 instances and moving to a cleaner, automated setup using Terraform and Ansible. But before wiping anything, I made sure ...
Objective: Building a proper user interface to use AWS credentials, then monitor everything with Prometheus and Grafana, deployed via Ansible. What I Did This Week: Built Full Frontend Stack (Flask + DynamoDB) Since S3 isn’t ideal for user data (espe...