2nd Workshop on AI in Networks and Distributed Systems

6th November 2020, Milan, Italy, remote presentation only

The workshop will take place at 8 am EST (2 pm CET) on Webex conferencing tool


8.00 – 8.55 NYC / 14.00 – 14.55 Milan

  • WAIN Chairs – Welcome message
  • Keynote: Nicholas Lane – Flower: A Friendly Federated Learning Research Framework… and a first look into the carbon footprint of federated methods

9.00 – 9.55 NYC / 15.00 – 15.55 Milan

  • Md Rajib Hossen and Mohammad Islam. Mobile Task Offloading Under Unreliable Edge Performance
  • Nikolas Wehner, Michael Seufert, Joshua Schüler, Sarah Wassermann, Pedro Casas and Tobias Hoßfeld. Improving Web QoE Monitoring for Encrypted Network Traffic through Time Series Modeling
  • Oezge Celenk, Thomas Bauschert and Marcus Eckert. Machine Learning based KPI Monitoring of Video Streaming Traffic for QoE Estimation

10.00 – 10.55 NYC / 16.00 – 16.55 Milan

  • Dena Markudova, Martino Trevisan, Paolo Garza, Michela Meo, Maurizio Munafò and Giovanna Carofiglio. What’s my App? ML-based classification of RTC applications
  • Gastón García González, Pedro Casas, Alicia Fernández and Gabriel Gómez. On the Usage of Generative Models for Network Anomaly Detection in Multivariate Time-Series
  • Shunsuke Higuchi, Junji Takemasa, Yuki Koizumi, Atsushi Tagami and Toru Hasegawa. Feasibility of Longest Prefix Matching using Learned Index Structures


Keynote speech – Nicholas Lane

Flower: A Friendly Federated Learning Research Framework… and a first look into the carbon footprint of federated methods 

Federated Learning (FL) has emerged as a promising technique for edge devices to collaboratively learn a shared prediction model, while keeping their training data on the device, thereby decoupling the ability to do machine learning from the need to store the potentially privacy sensitive user data in the cloud. However, despite rapid the progress made in FL in recent years, it still remains far too difficult to evaluate FL algorithms under a full range of realistic system constraints (viz. compute, memory, energy, wired/wireless networking) and scale (thousands of federated devices and larger). As a consequence, our understanding of how these factors influence FL performance and should shape the future evolution of FL algorithms remains in a very underdeveloped state.

In this talk, I will describe how we have begun to address this situation by developing Flower — an open-source framework built to help bridge this gap in evaluation and design. Through Flower, it becomes relatively simple to measure the impact of common real-world FL situations, such as if participating devices have limited compute resources (e.g., an embedded device), or when network speeds are highly varied and unstable. I will highlight early empirical observations, made using Flower, as to what the implications are for existing algorithms under the types of heterogeneous large-scale FL systems we anticipate will increasingly appear. Finally, to showcase the potential and flexibility of Flower, I will show how it can even be used to make assessments of the carbon footprint of FL in various settings — to the best of our knowledge, this is the first time FL has been studied from the perspective of its environmental impact. 

Nic Lane is a Senior Lecturer (Associate Professor) in the department of Computer Science and Technology at the University of Cambridge where he leads the Machine Learning Systems Lab (CaMLSys). Alongside his academic role, he is also a Director (On-Device and Distributed Machine Learning) at the Samsung AI Center in Cambridge. ).


Submission deadline: August 22nd, 2020 Extended to September 15, 2020, papers must be 3-4 pages long

Author registration: 50€

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: August 22, 2020 (Anywhere on Earth) Extended to September 15, 2020 (AoE)

Notification of acceptance: September 25, 2020 Extended to October 7, 2020

Camera ready version deadline: October 15, 2020

Workshop day: November 6, 2020

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.


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
  • Tian Guo, Worcester Polytechnic Institute, USA
  • Xin Liu, UC Davis, USA
  • Yanhua Li, Worcester Polytechnic Institute, USA