Resilient Edge-Cloud Autonomous Learning with Timely inferences

Project Advisor and Mentors: Professor Anand Sarwate, Professor Waheed Bajwa, Nitya Sathyavageeswaran, Vishakha Ramani, Yu Wu, Aliasghar Mohammadsalehi, Muhammad Zulqarnain

Team: Shreya Venugopaal, Haider Abdelrahman, Tanushree Mehta, Lakshya Gour, Yunhyuk Chang

Objective: The purpose of this project is to use ORBIT in order to design and run experiments which will analyze the training and prediction of various ML models across multiple edge devices. Additionally, students will develop a latency profiling framework for MEC-assisted machine learning using cutting edge techniques such as splitting models across multiple orbit nodes and networks, as well as early exit. They will then analyze these models for latency and accuracy tradeoff analysis, along with measuring network delays.

Week 1

Summary

• Understood the goal of the project.
• Installed packages and wired a cluster of 8 servers.
• Got familiar with Linux by practicing some simple Linux commands.

Next Steps

• Create and train a “small” and “large” Neural Network
• Attempt to simulate the difference between their performances at inference

Week 2

Summary Next Steps

Week 3

Summary Next Steps

Week 4

Summary Next Steps

Links to Presentations

• Week 1
• Week 2
• Week 3
• Mid Sem
• Week 4
• Week 5
• Final

Goals: Summary:

• Created and Trained a ‘Small’ and ’Large’ Neural Networks
• Compared their performances on the CIFAR10 dataset
• Established connection between two nodes
• Communicated test data between nodes to compare accuracy and delay between our NN models
• Need to serialize by bytes instead of transferring as strings
• Had a discussion on various research papers related to our project

Next Steps:

• Send the data as bytes instead of strings
• Calculate the times for transfer & processing
• Read more papers related to stuff about early exit and edge cloud computing
• Divide data into “chunks” for faster and more efficient transmission
• Design experiments to test different architectures and implementations of Early Exiting and Split Computing
• Track and add Age of Information Metrics

Presentation links:

• Link - Lakshya
• Link - Shreya
• Link - Haider
• Link - James
• Link - Tanushree