@inproceedings{c655737a14a24ce88b6bbf116e50ef5f,
title = "Experimenting an Edge-Cloud Computing Model on the GPULab Fed4Fire Testbed",
abstract = "There are various open testbeds available for testing algorithms and prototypes, including the Fed4Fire testbeds. This demo paper illustrates how the GPULAB Fed4Fire testbed can be used to test an edge-cloud model that employs an ensemble machine learning algorithm for detecting attacks on the Internet of Things (IoT). We compare experimentation times and other performance metrics of our model based on different characteristics of the testbed, such as GPU model, CPU speed, and memory. Our goal is to demonstrate how an edge-computing model can be run on the GPULab testbed. Results indicate that this use case can be deployed seamlessly on the GPULAB testbed.",
keywords = "Edge Computing, IoT, Machine Learning",
author = "Vikas Tomer and Sachin Sharma",
note = "Publisher Copyright: {\textcopyright} 2022 IEEE.; 28th IEEE International Symposium on Local and Metropolitan Area Networks, LANMAN 2022 ; Conference date: 11-07-2022 Through 12-07-2022",
year = "2022",
doi = "10.1109/LANMAN54755.2022.9820006",
language = "English",
series = "IEEE Workshop on Local and Metropolitan Area Networks",
publisher = "IEEE Computer Society",
booktitle = "28th IEEE International Symposium on Local and Metropolitan Area Networks, LANMAN 2022",
address = "United States",
}