Machine Learning for Edge-Cloud Systems (ML4ECS)
CfP
In conjunction with HiPEAC 2025
HiPEAC 2025 workshop
January 22, 2025
Barcelona, Spain
Autonomic end-to-end system management across the cloud-edge continuum is becoming increasingly critical as computational demands, scale, dynamics, and system heterogeneity grow rapidly. Machine Learning for Edge-Cloud Systems (ML4ECS) is a full-day workshop that focuses on academic and industrial innovation and research on developing and deploying AI/ML models to enable optimized system management and application execution on the continuum.
The workshop aims to bring together a community of researchers and practitioners who study problems at the intersection of ML, Edge-Cloud continuum, distributed and dependable system operation, and flexible, adaptive, and resilient application deployment. It seeks to reach a broader community invested in this fast-evolving field, aiming to increase awareness of ongoing initiatives, encourage collaboration, and promote the open exchange of ideas.
Call For Papers
The workshop invites the research community to submit expressions of interest to provide a talk based on an extended abstract format focusing on topics such as (but not limited to):
- ML models to improve overall system performance metrics (such as latency, throughput, energy dissipation, use of green energy, security, etc.) across the Edge-Cloud continuum.
- ML models for efficient and transparent pooling of edge resources, seamless integration with cloud and edge infrastructures, resource configuration optimization, multi-level orchestration, workload migration, security, and anomaly detection.
- Privacy-preserving decentralized learning mechanisms and adaptive networking services.
- Learning mechanisms for sustainable computing with lower carbon intensity in Edge-Cloud operations.
- ML-based Open-source tools and frameworks for large-scale experimentation in edge systems.
- Multi-cluster, multi-domain environments and applications.
- Network communication and optimization across resource-restricted edge environments.
- AI/ML-enabled tools and mechanisms in resource-constrained environments.
- AI/ML-based Joint orchestration of cross-layer data (network, compute, data resources).
Papers will be evaluated by the workshop’s technical program committee based on the quality of the submission, its relevance to the workshop’s topics, and its potential to ignite discussions about future directions, and solutions related to the mentioned topics. We welcome research papers, case studies, and position papers, as well as demos.
Moreover, we will organize panel discussions at the end of each technical session to allow for more comprehensive exploration of complex topics and drive active participation from the audience through Q&As.
The workshop will present a Best Talk award, sponsored by the HE CODECO project.
There will be no formal proceedings of the workshop. The authors are free to include the content of the extended abstract in a paper submitted to a journal or conference with proceedings. The presentations will be made publicly available via this website and/or HiPEAC.
Important Dates
Extended Abstract submission: October 30 November 6, 2024, 23:59 AoE
Notification: November 10 November 15, 2024
Workshop: January 22, 2025
Paper Format
Papers should be in double column IEEE format up to 2 pages, not including references.
Papers should be uploaded as PDF.
Submissions are double blind.
Submission Site
Submissions will be made at easychair.org.
Program
TBA.
Workshop Organizers
Chair: Nikolaos Bellas (University of Thessaly, EL, and MLSysOps)
- Rute C. Sofia (fortiss GmbH and HE CODECO Coordinator)
- Raffaele Gravina (University of Calabria, IT, and MLSysOps)
- Jesus Omaña Iglesias (Telefónica and EDGELESS)
Technical Program Committee:
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- Ana Solange (INOVA +, PT)
- Josep Martrat (ATOS, ES)
- Vasileios Theodorou (Intracom-Telecom, EL)
- Vassilis Tsaoussidis (ATHENA, EL)
- Xiaoming Fu (University of Göttingen, DE)
- Harald Müller (Siemens AG, DE)
- John Soldatos (Netcompany-Intrasoft, LU)
- Marco Jahn (Eclipse Foundation, DE)
- Rizkallah Touma (I2CAT, ES)
- Dimosthenis Kyriazis (University of Piraeus Research Center, EL)
- Luis Miguel Contreras (Telefonica, ES)
- David Jiménez (Universidad Politecnica de Madrid, ES)
- Andries Stam (Almende)
- Josh Salomon (RedHat)
- Luis Garcés-Erice (IBM)
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- Adel N. Toosi (Monash University, AU)
- Borja Saez (Infineon, DE)
- Carlos Molina (Eurecat, ES)
- Chen Chen (University of Cambridge, UK)
- Emanuele Carlini (ISTI-CNR, IT)
- Francisco Vicente (Worldline Iberia SA, ES)
- Jesús Omaña Iglesias (Telefonica Research, ES)
- Manos Athanatos (Technical University of Crete, EL)
- Markus Sauer (Siemens AG, DE)
- Pablo Serrano (Universidad Carlos III de Madrid, ES)
- Raphael Hetzel (Technical University of Munich, DE)
- Roberto Riggio (Università Politecnica delle Marche, IT)
- Roman Kolcun (University of Cambridge, UK)
- Timothy Wood (George Washington University, USA)
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- Nikolaos Bellas (University of Thessaly, EL)
- Christos D. Antonopoulos (University of Thessaly, EL)
- Spyros Lalis (University of Thessaly, EL)
- Raffaele Gravina (University of Calabria, IT)
- Gianluca Aloi (University of Calabria, IT)
- Dimitrios Chatzopoulos (University College Dublin, IE)
- John Byabazaire (University College Dublin, IE)
- Anastasios Nanos (NUBIS, EL)
- Christos Panagiotou (NUBIS, EL)
- Marcel Feher (Chocolate Cloud, DK)