Started my Project on Energy-Efficient DevOps: Auto-Suspending Idle AWS Resources using ML

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:

ssh -i "yourinstancekey" ubuntu@<your-public-ip>
ssh: connect to host <your-public-ip> port 22: Connection timed out

Cause:
Security Group was too restrictive — it allowed SSH (port 22) only from a previous IP.

Fix:

• Navigated to EC2 > Security Groups

• Edited the Inbound Rules

• Changed port 22 to allow from My IP (current one)

• Connection successful via PowerShell

---

Problem 2: Installing awscli failed on Ubuntu 24.04

Command used:

pip3 install awscli --upgrade --user

Error:

error: externally-managed-environment
This environment is externally managed.

Why it happened:
Ubuntu 24.04 follows PEP 668, which prevents system-wide pip installs without a virtual environment.

Fix:

• Created a virtual environment:

    python3 -m venv myenv
    source myenv/bin/activate
    pip install awscli
  

Or use:

sudo apt install awscli

Screenshot:

---

What’s Working So Far:

• EC2 instance (Ubuntu 24.04 LTS)

• SSH access from Windows (PowerShell)

• Installed base packages:
  htop, stress, cron, python3-pip, awscli

• AWS CLI inside virtual environment

---

Tools Used

• AWS EC2 (Free Tier)

• Ubuntu 24.04

• PowerShell SSH

• Python 3.12

• AWS CLI (inside venv)

• Stress, Cron

---

Want to Follow Along?

I’ll be sharing weekly progress — issues, logs, architecture, and ML models.

If you've solved similar problems (like automated cloud optimization), I’d love to hear your insight.

---