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static
templates
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EC2_OnDemand.csv
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License.md
README.md
app.py
computeCost.py
config.py
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mapping.py
monitoring.py
optimizer.py
requirements.txt
resource_mining.py
user_data.yml

README.md

#CLOUD-METRIC

Cloud-Metric is an application framework for estimating cost of running applications on Amazon Web Services and Google Cloud Platform. It is quite portable and lightweight. It is designed and developed for software engineers who want to see during development phase how their application is doing in terms of cost on the public clouds - AWS and GCP and also for researchers in academia with enough funding who want to migrate their (on-premise) applications to public clouds.

###Features Cloud-Metric provides the following features:

Resource Mining - meters user's development environment and output the results on application interface.

Resource Monitoring - monitors instances resources such as CPU, Memory, Disk storage, provides monitoring metrics for both inividual machines and entire clusters.

Cost Estimation - provides monthly on-demand cost estimates of the metered environment on Amazon Web Services and Google Cloud Platform. provides individual machines monthly on-demand costs and cost of entire cluster. It also provide users the ability to see the varying costs of instances on all regions in AWS and GCP.

Instance Matching - matches each machine in user's environment to the closest matching instance type on AWS and GCP

Instance Recommendation - Cloud Metric provides recommendation of instances types on AWS and GCP based on the resource utilisation on the user's development environment

###Tools

Cloud Metric is developed in Python 2.7, Flask, and a MongoDB.

###Deployment Cloud Metric is composed of two components:

  • Flask App
  • External mining and monitoring scripts

Here I am going to show you the steps required to deploy cloud metric app in Docker container using Docker Compose

We are ready to go! :shipit:

I assume that you are in your ubuntu virtual machine

####Step 1: Install docker by running the following commands:

  1. sudo aptitude update

  2. sudo aptitude -y upgrade

  3. sudo aptitude install linux-image-extra-'uname -r

  4. sudo sh -c "wget -qO- https://get.docker.io/gpg | apt-key add -"

  5. sudo sh -c "echo deb http://get.docker.io/ubuntu docker main\ > /etc/apt/sources.list.d/docker.list"

  6. sudo aptitude update

  7. sudo aptitude install lxc-docker

####Step 2: Run the following commands to install Docker Compose

  • sudo apt-get install python-pip

  • pip install docker-compose

####Step 3: Running the application Create a directory to store the application

mkdir [directoryname]

cd [directoryname]

Use git to clone the app from the repo

git clone https://github.com/ajallow07/Cloud_Cost_Estimator

cd Cloud_Cost_Estimator

Build the an image using docker compose

sudo docker-compose build

Start the application by

sudo docker-compose up

Go to your browser and type in http://[your container IP]:5000, you should see the application interface!

Now you got cloud metric running the app and mongodb in a single docker container ๐Ÿ‘ ๐Ÿ‘ ๐Ÿ‘

####Step 4: External Mining and Monitoring files

To register you environment on cloud metric app, you need to run:

resource_mining.py - which meters your individual instances and sends the data to the app's monogDB

monitoring.py - which retrieve the resources (CPU, Memory, and Disk) utilization in your instance and send the data to app's monogoDB every 60 seconds. You can change this value to anything value you want.

To successfully run this files you need to install pymongo, and psutil by using the following commands:

sudo apt-get install python-pymongo

sudo apt-get install python-psutil

Once installation is done you simply run the resource_miner.py once by typing:

python resource_mining.py [IP of your mongoDB] [Your Cluster Name]

and run monitoring.py as a process by typing:

python monitoring.py [IP of your monogDB] [Your Cluster Name]

Congratulations! You've got your environment monitored and meter by Cloud Metric ๐Ÿ‘๐Ÿ‘๐Ÿ‘