ZYN-z/CSI4101-software-capstone-design

Network aware load balancing for edge/cloud computing using deep reinforcement learning

A network-aware load balancer using DRL

Scenario:

1. Client query load balancing server for inferencing server address.
2. Load balancer return serving address using DRL.
3. Client send actual request to inferencing server.
4. Add artifical network overhead upon receiving inferencing response.
5. Return overall results to evaluater to generate reward for DRL.

Things To Do

1. Make test request series and evaluater

2. Implement random load balancer

3. Implement region aware load balancer

4. Run drl for 1 an hour

5. Compare the result of 3 load balancer

6. Automate deployment.

   1. Include dockerfile of the service server. Also probably include some bash command for multiple docker each serving a single model.
   2. Scripts to install prerequisite.

client.py:

1. Load ImageNet validation images.
2. Query loadbalancer.py for inferencing server address.
3. Send request to inferencing server.
4. Calculate artficial network overhead.
5. Send result to evaluater.py.

loadbalancer.py:

1. Generate 'oberservation' for DRL upon client query for serving address.
2. Return DRL's action(serving address) to client.

evaluater.py:

1. Load ImageNet validation solutions.
2. Listen from client response reports, parse and feed to drl.py

servermonitor.py

1. Listen for state reports from servermonitor.py
2. Send gathered state reports to drl.py

reportstate.py:

1. Collect server's state.
2. Send it to servermonitor.py.

drl.py:

1. Uses request and server_state as state
2. DQN to aprox. q function
3. Return server/model as action