AWS Image Recognition as a Service

Project 1 - IaaS - Amazon Web Services

• The aim of the project was to develop a cloud app which will provide Image Recognition as a Service to users by using the AWS cloud resources to perform deep learning on images provided by the users.
• The deep learning model was provided in an AWS image (ID: ami-07303b67, Name: imageRecognition, Region: us-west-1a). This application invokes this model to perform image recognition on the received images.
• The application will handle multiple requests concurrently. It will automatically scale out when the request demand increases, and automatically scale in when the demand drops.
• AWS services used in the project are
  • Elastic Compute Cloud (EC2)
  • Simple Queue Service (SQS)
  • Simple Storage Service (S3)
• Further details are provided in the report.

Web-Tier-AWS

• This is a RESTful Web Service which accepts requests from the user (Image URL) and puts the request body onto an Input Queue.
• After which, it starts listening to the Output Queue for the response.
• This application also has a load balancer service which creates app instances when the request demand increases (Scale out).

Listener

• This application runs inside the app instances and listens for messages (requests) in the Input Queue.
• When the message arrives, it takes the message and runs the deep learning model for classification and puts the classification result into a S3 bucket. The classification result is also inserted into the Output Queue.
• When there is no message in the Input queue, the application shuts down the instance in which its running, facilitating scale in.

Listener Running

• This is the same as Listener application but the instance which is running this application won't terminate at all, facilitating quick response to the user.

Instructions:-

• HTTP Requests should be sent in the below format
• http://[IP Address]/cloudimagerecognition?input=[URL of the image]
• example: http://13.57.186.192:8080/cloudimagerecognition?input=http://visa.lab.asu.edu/cifar-10/104_automobile.png