WAIN


2nd Workshop on AI in Networks and Distributed Systems

6th November 2020, Milan, Italy, remote presentation will be possible

Submission deadline: August 22nd, 2020, papers must be 3-4 pages long

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 is 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, just 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 smart 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.

a

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 
  • 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

a

Important dates:

Submission deadline: August 22, 2020 (Anywhere on Earth)

Notification of acceptance: September 25, 2020

Camera ready version deadline: October 15, 2020

Workshop day: November 6, 2020

a

Submission Guidelines:

Papers will be published at ACM SIGMETRICS Performance Evaluation Review (PER, https://www.sigmetrics.org/per.shtml, 3 to 4 pages long).

Submissions must be original, unpublished work, and not under consideration at another conference or journal. The format for the submissions is that of PER (two-column 10pt ACM format)), between 3 and 4 pages, including all figures, tables, references, and appendices. Papers must include authors names and affiliations for single-blind peer reviewing by the TPC. Authors of accepted papers are expected to present their papers at the workshop.

PER style file can downloaded from http://www.sigmetrics.org/sig-alternate-per.cls. Please change the argument of the command \conferenceinfo to \conferenceinfo{Workshop on AI in Networks and Distributed Systems (WAIN) 2020}{~~~Milan,Italy}.

The submission page is available at https://easychair.org/conferences/?conf=wain2020.

a

Chairs

Luca Vassio, Politecnico di Torino, Italy

Zhi-Li Zhang, University of Minnesota, US

Danilo Giordano, Politecnico di Torino, Italy

Abhishek Chandra, University of Minnesota, US

Publicity Chair

Martino Trevisan, Politecnico di Torino, Italy

TPC members

  • Ali Butt, Virginia tech, USA
  • Ali Safari, Western University, Toronto
  • Ana Paula Couto da Silva, Universidade Federal de Minas Gerais, Brazil
  • Andrea Morichetta, TU Wien, Austria
  • Baochun Li, University of Toronto, Canada
  • Carlos Henrique Gomes Ferreira, Federal University of Ouro Preto, Brazil
  • Dan Li, Tsinghua University, China
  • Daniel Sadoc Menasche, Federal University of Rio de Janeiro Brazil
  • Edmundo de Souza e Silva, Federal University of Rio de Janeiro, Brazil
  • Giuliano Casale, Imperial College, UK
  • Giuseppe Siracusano, NEC Heidelberg, Germany
  • Jinoh Kim, Texas A&M University-Commerce, USA
  • Marco Mellia, Politecnico di Torino, Italy
  • Mario Almeida, Samsung AI Center Cambridge, UK
  • Ming Zhao, Arizona State University, USA
  • Ramesh Sitaramen, University of Massachusetts Amherst, US
  • Roberto Bifulco, NEC Heidelberg, Germany
  • Roderick Fanou, CAIDA, USA
  • Tian Guo, Worcester Polytechnic Institute, USA
  • Xin Liu, UC Davis, USA
  • Yanhua Li, Worcester Polytechnic Institute, USA