Resource optimization with DRL-driven real time service placement strategy in Edge-cloud continuum.

In a smart home environment, we are using cloud computing for powerful processing and storage of big data that are generated from IoT devices. Further we are also using Fog to handle the data in a more real time manner. Whenever we request for any service, the service provider take advantage of the resource available at fog and cloud. Fog resources are used to provide more real-time, sensitive services that require less computing and storage resources. On the other hand, cloud resources are used to keep the data (big data) for future purpose and process them. Sometimes, it is difficult to fulfil all SLA constraints only with the cloud resource and that's where the fog resource comes into the picture.

This thesis topic mainly focuses on efficient utilisation of cloud and fog resources. An AI model (most probably the DRL) needs to be developed to decide when to use fog computing and when to use cloud computing.

DRL-based Service Placement

• Source Code

Experiment Applications

1) Application 1 - Deep learning based object classification using YOLOv3

• Client Side
• Server Side
• Testing scripts
  • curl -X POST -F image=@traffic.jpg 'http://$IP:$PORT/api/detect' --output traffic_yolo.png

2) Application 2 - Text-audio synchronisation or forced alignment (Aeneas)

• Client Side
• Server Side
• Testing scripts
  • python3 -m aeneas.diagnostics
  • python3 setup.py build_ext --inplace
  • python3 aeneas_check_setup.py
  • curl -X POST -F audio=@p001.mp3 -F subtitle=@p001.xhtml 'http://$IP:$PORT/api/sync' --output p001.json

3) Application 3 - Speech-to-text conversion (PocketSphinx)

• Client Side
• Server Side
• Testing scripts
  • curl -X POST -F audio=@p001.mp3 'http://$IP:$PORT/api/sync' --output output.txt

Deployment Instructions

• Clone project:

git clone git@github.com:chinmaya-dehury/AI4FogCloudServiceDisperse.git

Deployment of Environments

• Goto nodes_apis
• In Smart Gateway, run:

./execute.sh smartgateway.py

• In Fog Master Node, run:

./execute.sh fog_master_node.py

• In Fog Worker Node, run:

./execute.sh fog_worker_node.py

• In Cloud, run:

./execute.sh cloud.py

Deployment of Application 1

• Goto app1_yolo/app1_server
• In Fog, run:

docker build -t app1-fog -f DockerfileFog .
docker run -d -p 0:5000 app1-fog:latest

• In Cloud, run:

docker build -t app1-cloud -f DockerfileCloud .
docker run -d -p 0:5000 app1-cloud:latest

Deployment of Application 2

• Goto app2_aeneas/app2_server
• In Fog, run:

docker build -t app2-fog:latest .
docker run -d -p 0:5000 app2-fog:latest

• In Cloud, run:

docker build -t app2-cloud:latest .
docker run -d -p 0:5000 app2-cloud:latest

Deployment of Application 3

• Goto app3_pocketsphinx/app3_server
• In Fog, run:

docker build -t app3-fog:latest .
docker run -d -p 0:5000 app3-fog:latest

• In Cloud, run:

docker build -t app3-cloud:latest .
docker run -d -p 0:5000 app3-cloud:latest

Deployment of DRL Algorithm

• Goto drl_algorithm
• Update IPs/PORTs of both environments and applications in h_configs.py
• In Smart Gateway, run:

./s_install.sh
./s_execute.sh