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WAIN 2021 : 3rd International Workshop on AI in Networks and Distributed Systems

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Link: https://www.performance2021.deib.polimi.it/wain/
 
When Nov 8, 2021 - Nov 12, 2021
Where Milan, Italy and online
Submission Deadline Sep 2, 2021
Notification Due Oct 7, 2021
Final Version Due Oct 22, 2021
Categories    computer networks   distributed system   machine learning   artificial intelligence
 

Call For Papers

Thanks to rapid growth in network bandwidth and connectivity, networks and distributed systems have become critical infrastructures that underpin much of today's Internet services. They provide services through the cloud, monitor reality with sensor networks of IoT devices, and offer huge computational power with data centers or edge and fog computing. At the same time, AI and Machine Learning are being widely exploited in networking and distributed systems. Examples are algorithms and solutions for fault isolation, intrusion detection, event correlation, log analysis, capacity planning, resource management, scheduling, and design optimization, to name a few.

The scale and complexity of today's networks and distributed systems make their design, analysis, optimization, and management a daunting task. For this, smart and scalable approaches leveraging machine learning solutions must be deployed to take full advantage of these networks. WAIN workshop aims at showing to the community new contributions in these fields. The workshop looks for innovative approaches and use cases for understanding when and how to apply AI. WAIN will allow researchers and practitioners to share their experiences and ideas and discuss the open issues related to the application of machine learning to computer networks.


Topics of Interest:

The following is a non-exhaustive list of topics of interest for WAIN workshop:
• Applications of ML in communication networks and distributed systems
• Data analytics and mining in networking and distributed systems
• Traffic monitoring through AI
• AI applied to IoT and 5G
• Application of reinforcement-learning
• ML-based methodologies for anomaly detection and cybersecurity
• Performance optimization through AI/ML and Big Data
• Experiences and best-practices using machine learning in operational networks
• Reproducibility of AI/ML in networking and distributed systems
• Methodologies for performance evaluation of distributed infrastructure
• Machine Learning application in cloud, edge, and fog computing
• Performance evaluation of Content Delivery Networks
• Application of AI/ML in sensor networks
• AI/ML for data center management
• AI/ML for cyber-physical systems
• ML-driven resource management and scheduling
• AI-driven fault tolerance in distributed systems

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