This Python project automates the provisioning and monitoring of web servers on AWS, simplifying the process of EC2 instance launch, S3 bucket setup, and website deployment with an easy-to-use script.

• Utilises Python 3 and the Boto3 library to interact with AWS services.
• Automates the launch of an Amazon Linux EC2 nano instance, automatically determining the latest AMI using AWS Systems Manager Parameter Store.
• Configures security groups and tags for easy management and security.
• Installs and starts a web server to display instance metadata (with potential customization).
• Configures an S3 bucket for static website hosting, enabling content uploads and public access.
• Opens a browser to show the newly deployed EC2 instance and S3 static website URLs.
• Facilitates enhanced EC2 instance monitoring with an included monitoring.sh script and SSH-based automation.
• Integrates with AWS CloudWatch to list metrics, retrieve granular metric data over various time periods, and manage alarms.
• Utilises a configuration file (config.ini) for customisation.
• Includes error handling to improve robustness.

• Python 3 installed on your local machine (https://www.python.org/).
• Boto3 library installed (pip3 install boto3).
• Python Fire installed (pip3 install fire).
• An AWS account with properly configured credentials (typically in ~/.aws/credentials).
• An existing SSH key pair for EC2 access, placed in the same directory as the script.

1. Clone the Repository: Download the project to your local machine.

2. Configure AWS Credentials: Verify that your AWS credentials are correctly set up.

3. Customise the Configuration: Modify the config.ini file to customise the script's behaviour. Before executing most commands, ensure you've updated config.ini with your AWS PEM key name and other desired settings.

4. Getting Help: To view available commands and get help with a specific command, use the --help or -h flag. For example: python3 devops_1.py --help

5. Deployment: To launch an EC2 instance, configure an S3 bucket, and deploy the web server, execute python3 devops_1.py with no arguments.

6. CLI: To interact with AWS resources using the command-line interface, try some of the commands below.

AWS Resource Management

• AWS Management Commands:

  • python3 devops_1.py instances
  • python3 devops_1.py terminate <instance_id>
  • python3 devops_1.py terminate_all
  • python3 devops_1.py buckets
  • python3 devops_1.py delete_buckets

• CloudWatch Commands:

  • python3 devops_1.py cloudwatch --help
  • python3 devops_1.py cloudwatch list_metrics --instance_id <instance_id>
  • python3 devops_1.py cloudwatch get_metric_data --instance_id <instance_id> --metric_name <metric_name> [--period <1h|8h|24h>]
  • python3 devops_1.py cloudwatch create_alarm --alarm_name <name> --metric_name <metric_name> --instance_id <instance_id> --threshold <threshold> [--comparison_operator <operator>] [--evaluation_periods <number>] [--period <seconds>] [--statistic <statistic>]
  • python3 devops_1.py cloudwatch list_alarms
  • python3 devops_1.py cloudwatch delete_alarm --alarm_name <name>
  • python3 devops_1.py cloudwatch metrics --instance_id <instance_id>

Examples

• Retrieve an overview core metrics for an instance:

  python3 devops_1.py cloudwatch metrics --instance_id i-0c86e59079b90dd57

• Retrieve 24 hours of network traffic data:

  python3 devops_1.py cloudwatch get_metric_data --instance_id i-0c86e59079b90dd57 --metric_name NetworkIn --period 24h

• Check disk read operations over the past hour:

  python3 devops_1.py cloudwatch get_metric_data --instance_id i-0c86e59079b90dd57 --metric_name DiskReadOps --period 1h

• Create an alarm that triggers if CPU usage exceeds 90%:

  python3 devops_1.py cloudwatch create_alarm --alarm_name HighCPUAlert --metric_name CPUUtilization --instance_id i-0c86e59079b90dd57 --threshold 90

To leverage the included monitoring.sh script for system monitoring on the EC2 instance:

1. The devops_1.py script will automatically transfer monitoring.sh to the instance upon execution.
2. SSH into the EC2 instance using the .pem key file.
3. Change the script's permissions to make it executable: chmod +x monitoring.sh.
4. Execute the script: ./monitoring.sh.