3rd International Workshop on AI in Networks and Distributed Systems

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.

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

Important dates:

Submission deadline: September 2, 2021September 16, 2021 (Anywhere on Earth)

Notification of acceptance: October 7, 2021

Camera ready version deadline: October 22, 2021

Workshop day: November 8 – 12, 2021

Submission Guidelines:

Papers will be published at ACM SIGMETRICS Performance Evaluation Review (PER, https://www.sigmetrics.org/per.shtml). 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), maximum 5 pages + references. 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 be 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) 2021}{~~~Milan,Italy}.

The submission page is available at https://easychair.org/conferences/?conf=wain2021.


Luca Vassio, Politecnico di Torino, Italy

Danilo Giordano, Politecnico di Torino, Italy

Jinoh Kim, Texas A&M University-Commerce, US

Jon Crowcroft, University of Cambridge, UK

Publicity Chair

Martino Trevisan, Politecnico di Torino, Italy

TPC Members

Abhishek Chandra, University of Minnesota, USA

Ana Paula Couto da Silva, Universidade Federal de Minas Gerais, Brazil

Andrea Morichetta, waiTU Wien, Austria

Baochun Li, University of Toronto, Canada

Carlos Henrique Gomes Ferreira, Universidade Federal de Ouro Preto, Brazil

Chunglae Cho, Electronics and Telecommunications Research Institute, South Korea

Edmundo de Souza e Silva, Federal University of Rio de Janeiro, Brazil

Eiko Yoneki,University of Cambridge, UK

Eric  Chan-Tin, Loyola University Chicago, USA

Giuseppe Siracusano, NEC Heidelberg, Germany

Hamed Haddadi, Imperial College, UK

Jerry Chou, National Tsing Hua University, Taiwan

Laurent Bindschaedler, Massachusetts Institute of Technology, USA

Mário Almeida, Samsung AI in Cambridge, UKa

Martino Trevisan, Politecnico di Torino, Italy

Nour Moustafa, University of New South Wales, Australia

Sang-Yoon Chang, University of Colorado at Colorado Springs, USA

Tian Guo, Worcester Polytechnic Institute, USA

Zhi-Li Zhang, University of Minnesota, USA

Zied Ben Houidi, Huawei Technologies Co. Ltd, France