(We apologize if you receive multiple copies of this call.)
The 3rd Workshop on Edge Systems, Analytics and Networking
(EdgeSys'20)  co-located
with ACM EuroSys 2020
April 27-30, 2020 | Heraklion, Crete, Greece
https://edge-sys.github.io/2020/
Given the growing demand for real-time gathering and processing of vast
amount of data from data-intensive services such as autonomous driving and
augmented reality (AR), we are witnessing a visible trend to push computing
and data analytics closer to the edge of networks for benefits in low
latency, reliability, throughput, security and privacy. Supported by
lightweight virtualization technologies such as Docker Containers and
Unikernels, the decentralized edge computing paradigm aims to offer
efficient access to various geographically distributed computing resources.
In this context, the EdgeSys workshop focuses on the confluence of edge
computing, decentralized communication and distributed computing. For
example, there are several industry-led edge computing and orchestration
platforms such as KubeEdge, Firecracker, and Azure Sphere. In the
decentralized communication space, systems such as Mastodon and Matrix have
been developed. There are also lots of academic research activities on
mobile data and computation offloading (e.g., Cloudlet, Talket, ThinkAir,
MAUI, and MADNet) and systems that combine decentralization and
computation/storage offloading together, such as IPFS. In addition, as data
analytics and machine learning are increasingly offered as a service, the
next phase of evolution is to extend the offerings beyond basic algorithms
and push the analytics closer to the edge where the data from users and
devices is first captured. In this regard, edge computing has the potential
to enable a new class of real-time data analytics platforms and services.
We expect such edge-driven data analytics will emerge and reshape the
existing cloud-based data processing pipelines. The knowledge from this new
edge pipeline can further power future cyber-physical and network services,
such as cognitive assistance and proactive accident prevention for
autonomous driving.

The 3rd Workshop on Edge Systems, Analytics, and Networking (EdgeSys'20)
aims to bring together system researchers, data scientists, engineers and
practitioners to identify open directions and discuss the latest research
ideas and results on edge systems, analytics, and networking, especially
those related to novel and emerging technologies and use cases. EdgeSys
focuses on systems, analytics, and networking aspects, covering system
architecture, distributed ML algorithms, decentralized networking,
distributed consensus and ledger techniques, edge services and data
analysis.

The topics include but are not limited to the following:

- System Architecture for Edge Computing
- Serverless and In-network Computing (P4)
- Edge Storage Systems
- Edge-driven Data Analytics
- Distributed Machine Learning for Edge Analytics and Services
- Edge Security and Privacy
- Lightweight Virtualization for Edge Computing
- Distributed Consensus Algorithms and Ledger Technologies
- Infrastructure and Toolkit for Edge Computing and Analytics
- System Performance and Measurement
- Edge Networking and IoT Communications
- (Autonomous) Management for Edge Systems
- Security and Trust Management


Workshop & TPC Chairs:
Aaron Ding (TU Delft, Netherlands)
Richard Mortier (University of Cambridge, UK)

Steering Committee:
Jon Crowcroft (University of Cambridge, UK)
Henning Schulzrinne (Columbia University, USA)
Steve Uhlig (Queen Mary University of London, UK)
Peter Pietzuch (Imperial College London, UK)
Dieter Kranzlmüller (University of Munich / LRZ, Germany)
Dirk Kutscher (Hochschule Emden/Leer, Germany)

Technical Program Committee:
Babak Alipour (Apple, USA)
Yang Chen (Fudan University, China)
Jana Giceva (TU Munich, Germany)
Ionel Gog (UC Berkeley, USA)
Yao Guo (Peking University, China)
Tim Harris (Amazon, UK)
Volker Hilt (Nokia Bell Labs, Germany)
Kleomenis Katevas (Imperial, UK)
Asterios Katsifodimos (TU Delft, Netherlands)
Nic Lane (University of Oxford, UK)
Ilias Leontiadis (Samsung AI, UK)
Yong Li (Tsinghua, China)
Jonathan Mace (Max Planck Institute, Germany)
Jukka Manner (Aalto, Finland)
Roberto Morabito (Princeton, USA)
Diana Popescu (University of Cambridge, UK)
Qifan Pu (Google, USA)
Etienne Rivière (UCLouvain, Belgium)
Andreas Seitz (Siemens, Germany)
Maarten van Steen (University of Twente, Netherlands)
Fusang Zhang (Chinese Academy of Science, China)
Marco Zimmerling (TU Dresden, Germany)
Matthias Waehlisch (FU Berlin, Germany)
Michael Welzl (University of Oslo, Norway)
Tianyin Xu (UIUC, USA)

Publicity Chair:
Suzan Bayhan (University of Twente, Netherlands)

Web Chair:
Vittorio Cozzolino (TU Munich, Germany)

Submission Guidelines:
Authors are encouraged to submit papers describing original and
unpublished research, not currently under review in other venues. In
particular, innovative, early-stage ideas and preliminary results are
welcome. Papers must be submitted electronically as PDF files, formatted
for 8.5x11-inch paper. The length of the paper must be no more than 6
pages in the ACM double-column format (10-pt font), including references
and everything. The authors are suggested to use the ACM Master article
template. The reviews will be single-blind. The first page must contain
an abstract, the name(s) and affiliation(s) of the author(s), as well as
the corresponding contact information. Each submission will receive
independent, blind peer reviews from the program committee. Accepted
papers will be published in the ACM Digital Library. At least one of the
authors of every accepted paper must register and present the paper at
the workshop.

The program committee will select one paper for the Best Paper Award.
*Submission deadline*: 28 February 2020
*Acceptance notification*: 20 March 2020
Camera ready: 3 April 2020
Workshop date: 27 April 2020
Submission Link: https://edgesys20.hotcrp.com/
Suzan Bayhan
Design and Analysis of Communication Systems (DACS)
University of Twente, The Netherlands
Office: ZI-5049 (Zilverling, 5th floor)
https://suzanbayhan.github.io/ <https://suzanbayhan.github.io/>
<https://suzanbayhan.github.io/>

https://www.utwente.nl/en/eemcs/dacs/
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