As the cloud extends to the fog and to the edge, computing services can be scattered over a set of computing resources that encompass users’ devices, the cloud, and intermediate computing infrastructure deployed in between. Moreover, increasing networking capacity promises lower delays in data transfers, enabling a continuum of computing capacity that can be used to process large amounts of data with reduced response times. Such large amounts of data are frequently processed through machine learning approaches, seeking to extract knowledge from raw data generated and consumed by a widely heterogeneous set of applications. Distributed machine learning has been evolving as a tool to run learning tasks also at the edge, often immediately after the data is produced, instead of transferring data to the centralized cloud for later aggregation and processing.
The DML-ICC workshop aims to be a forum for discussion among researchers with a distributed machine learning background and researchers from parallel/distributed systems and computer networks. By bringing together these research topics, we look forward in building an Intelligent Computing Continuum, where distributed machine learning models can seamlessly run on any device from the edge to the cloud, creating a distributed computing system that is able to fulfill highly heterogeneous applications requirements and build knowledge from data generated by these applications.
Paper submission: October 01, 2021 (Extended, hard deadline)
Notification to Authors: 23 October , 2021
Camera ready submission: 31 October, 2021
Workshop date: Exact date to be determined (6-9 December 2021)
DML-ICC 2021 workshop aims to attract researchers from the machine learning community, especially the ones involved with distributed machine learning techniques, and researchers from the parallel/distributed computing communities. Together, these researchers will be able to build resource management mechanisms that are able to fulfill machine learning jobs requirements, but also use machine learning techniques to improve resource management in large distributed systems. Topics of interest include but are not limited to:
• Autonomic Computing in the Continuum
• Business and Cost Models for the Computing Continuum
• Complex Event Processing and Stream Processing
• Computing and Networking Slicing for the Continuum
• Distributed Machine Learning for Resource Management and Scheduling
• Distributed Machine Learning in the Computing Continuum
• Distributed Machine Learning applications
• Distribute Machine Learning performance evaluation
• Edge Intelligence models and architectures
• Federated Learning
• Intelligent Computing Continuum architectures and models
• Management of Distributed Learning Tasks
• Mobility support in the Computing Continuum
• Network management in the Computing Continuum
• Privacy using Distributed Learning
• Programming models for the Computing Continuum
• Resource management and Scheduling in the computing continuum
• Smart Environments (Smart Cities, Smart Buildings, Smart Industry, etc.)
• Theoretical Modeling for the Computing Continuum
Paper submission is electronic only. Authors should use the Easychair system. The DML-ICC workshop invites authors to submit original and unpublished work. Papers should not exceed 6 pages in ACM format. Additional pages might be purchased upon the approval of the proceedings chair. All selected papers for this workshop are peer-reviewed and will be published in IEEE Xplore and ACM Digital Library.
NEW Submissions continue to use a double column format for review based on the new single-column template to facilitate the new ACM production process.
Accepted papers will later be converted into single-column format through the ACM TAPS process and therefore need to use the new templates that are single-column by default. Switch them to double-column for authoring your paper. This is possible in both the Word and the LaTeX templates.
Word: Format - Page - Columns - set to 2
At least one author of each paper must be registered for the conference in order for the paper to be published in the proceedings. The conference proceedings will be published by the ACM and made available online via the IEEE Xplore Digital Library and ACM Digital Library.
Submission requires the willingness of at least one of the authors to register and present the paper.
Ian Foster (University of Chicago and Argonne National Laboratory, USA)
Filip De Turck (Ghent University, Belgium)
Luiz F. Bittencourt (University of Campinas, Brazil)
Atakan Aral, University of Vienna, Austria
Gabriel Antoniu, Inria, France
Rodrigo Calheiros, Western Sydney University, Australia
Valeria Cardellini, University of Rome Tor Vergata, Italy
Marilia Curado, University of Coimbra, Portugal
Ivana Dusparic, Trinity College Dublin, Ireland
Roch Glitho, Concordia University, Canada
Mohammadreza Hoseinyfarahabady, University of Sydney, Australia
Carlos Kamienski, Federal University of ABC, Brazil
Wei Li, University of Sydney, Australia
Zoltán Mann, University of Duisburg-Essen, Germany
Radu Prodan, University of Klagenfurt, Austria
Omer Rana, Cardiff University, United Kingdom
Christian Esteve Rothenberg, University of Campinas, Brazil
Rizos Sakellariou, University of Manchester, United Kingdom
Josef Spillner, Zurich University of Applied Sciences, Switzerland
Javid Taheri, Karlstad University, Sweden
Massimo Villari, University of Messina, Italy