This project was developed as final project for the course Applied Cloud Computing.

• celeryconfig.py: Celery configuration file
• tasks.py: Celery tasks definition file
• app.py: Flask application file exposing the airfoil calculations to the end-user
• cpu_monitor: File to run application demand evaluation

This folder contains CloudNaca and Airfoil binary files.

This folder contains the following YAML scripts for the application deployment using Heat orchestrator and Cloud-init contextualizer:

• airfoil_asg_g14.yaml: Airfoil stack with auto-scaling group
• airfoil_rg_g14.yaml: Airfoil stack with resource group
• client_g14.txt: Celery client contextualization file
• worker_rg_g14.yaml: Celery worker resource group
• worker_g14.txt: Celery worker contextualization file
• environment.yaml: Celery worker resource registration

• Using resource group: openstack stack create -t airfoil_rg_g14.yaml airfoil_stack_g14
• Using auto-scaling group: openstack stack create -t airfoil_asg_g14.yaml -e environment.yaml airfoil_asg_g14

You could also change the default values for any of the following parameters including --parameter "ParamName=ParamValue"

• image: Image to be used for compute instance
• flavor: Type of instance (flavor) to be used
• key: Name of key-pair to be used for compute instance
• public_network: Public network with floating IP addresses
• internal_network: Private network with local IP addresses
• workers: Number of celery worker nodes to be deployed
• sec_group_name: Assigned name for the security group

To monitor task execution using celery flower visit: <floatingip_client_node>:5555