FAST: AI-based Network Traffic Analysis and Load Balancing Framework Underlying SDN Clusters-Reference-Cited by-同舟云学术

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FAST: AI-based Network Traffic Analysis and Load Balancing Framework Underlying SDN Clusters

Published:2024-05-14 Issue: Volume: Page:82-87
ISSN:
Container-title:2024 8th International Conference on Smart Cities, Internet of Things and Applications (SCIoT)
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Author:

Shah Drishya¹,Shah Diya¹,Ramoliya Fenil¹,Gupta Rajesh¹,Shah Hetal Brijesh²,Tanwar Sudeep¹,Shahinzadeh Hossein³,Garg Deepak⁴

Affiliation:

1. Nirma University,Institute of Technology,Department of Comp. Sci. & Engg,Ahmedabad,India

2. Dharmsinh Desai University,Department of ECE,Nadiad,Gujarat,India

3. Amirkabir University of Technology Tehran Polytechnic,Department of Electrical Engg,Tehran,Iran

4. SR University,School of Comp. Science and AI,Warangal,Telangana,India

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx8/10570096/10570097/10570111.pdf?arnumber=10570111

Reference12 articles.

1. Blockchain and AI-based Secure Onion Routing Framework for Data Dissemination in IoT Environment Underlying 6G Networks

2. Achieving Robust Performance for Traffic Classification Using Ensemble Learning in SDN Networks

3. A Comparative Study of Traffic Classification Techniques for Smart City Networks

4. Data Traffic Classification in Software Defined Networks (SDN) using supervised-learning

5. Going Beyond DiffServ in IP Traffic Classification

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