[hpc-announce] CFP. FGCS. Special Issue on Edge-Cloud Solutions for Big Data Analysis and Distributed Machine Learning

[JESUS CARRETERO PEREZ]

CFP.  Future Generation Computer Systems Journal
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Special Issue on Edge-Cloud Solutions for Big Data Analysis and Distributed
Machine Learning
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Submission open.  Important Dates:

Deadline for paper submission: Nov 30, 2023

Latest acceptance deadline for all papers: Feb 29, 2024


Motivation and Scope

Recently there has been a widespread use of edge-cloud solutions to
efficiently collect and analyze large amounts of data generated by IoT
devices. In many application domains, such as urban mobility, smart cities,
healthcare, augmented reality, it is extremely useful to combine resources,
applications and services from the edge to the cloud, in order to better
support tasks that require real-time processing and analysis, low response
times, as well as large computing and storage resources. This approach can
help to reduce the latency and network congestion associated with
traditional cloud-based Big Data analysis techniques, as the processing can
be performed locally on edge devices before being sent to the cloud for
further analysis. Big data analysis on the Edge-Cloud involves using
advanced data analytics techniques and frameworks to process and process
data that is distributed across the infrastructure, having several
applications like predictive maintenance, real-time monitoring of
industrial processes, smart grid management, and personalized healthcare.

Edge-Cloud solutions are also proving to be very effective in the field of
distributed machine learning algorithms to distribute computation and data
across the edge and the cloud to achieve efficient, scalable and accurate
predictive models. This is a very promising approach that can help
organizations to develop intelligent applications that can operate in
real-time and make decisions autonomously. However, Big data analysis on
the Edge-Cloud also poses several challenges, such as data privacy and
security, interoperability, scalability, energy efficiency. Those
challenges must be addressed to provide efficient and scalable solutions
for data-intensive applications like federated learning, social data
analysis, smart city services, and text mining.

We invite original research articles, review articles, and technical notes
related to the area of Big Data Analysis and Machine Learning in Edge-Cloud
platforms. The objective of this special issue is to provide a venue for
researchers, academicians, and industry practitioners to present their
latest findings and share their ideas on the latest trends, challenges, and
opportunities in this field.

Topics of interest include, but are not limited to:

- Edge-Cloud architectures and infrastructures for Big Data analysis and
machine learning;
- Scalable and distributed machine learning algorithms for edge-cloud
computing environments;
- Programming models for Big Data analytics on the edge-cloud environments;
- Real-time data analytics on edge devices and cloud infrastructures;
- Federated learning and transfer learning on edge-cloud architectures;
- Security and privacy in edge-cloud computing for big data analysis and
machine learning;
- Case studies and real-world applications of Big Data analysis and machine
learning in Edge-Cloud computing;
- Edge-Cloud resource allocation and scheduling techniques for efficient
data processing;
- Edge-Cloud integration with blockchain technology for secure and
decentralized data processing;
- Energy-efficient edge-cloud computing solutions;
- Edge-cloud solutions for autonomous vehicles and smart transportation
systems;
- Performance optimization and load balancing in Edge-Cloud architectures;
- Emulation and/or simulation approaches for testing and evaluating large
scale Edge-Cloud applications;
- Edge-Cloud solutions for augmented and virtual reality applications;
- Edge-Cloud solutions for smart grid systems and renewable energy
integration;
- Edge-Cloud solutions for smart healthcare systems and medical
applications;
- Edge-Cloud solutions for disaster response and emergency management
systems.


Guest Editors

Domenico Talia Domenico Talia
University of Calabria, Italy.
talia at dimes.unical.it

Jesus Carretero Perez Jesus Carretero Perez
University Carlos III, Spain.
jcarrete at inf.uc3m.es

Loris Belcastro Loris Belcastro
University of Calabria, Italy.
lbelcastro at dimes.unical.it

Manuscript Submission Instructions

The FGCS’s submission system (
https://www.editorialmanager.com/FGCS/default.aspx ) is now open for
submissions to our Special Issue from May 20, 2023. When submitting your
manuscript please select the article type "VSI: Edge-Cloud for Big Data"

All submissions deemed suitable by the editors to be sent for peer review
will be reviewed by at least two independent reviewers. Once your
manuscript is accepted, it will go into production to be published in the
special issue.

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Prof. Jesus Carretero
Computer Architecture Professor
Computer Science and Engineering Dep. University Carlos III of Madrid
Avda. Universidad 30,  28911 Leganes, Madrid, Spain

Email: jesus.carretero at uc3m.es
Tel: +34 916249458.  Fax: +34 916249129
Web: http://arcos.inf.uc3m.es/~jcarrete
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