The Grid is a rapidly developing computing structure that allows components of our information technology infrastructure, computational capabilities, databases, sensors, and people to be shared flexibly as true collaborative tools. Over the last 3 years there has been a real explosion of new theory and technological progress supporting a better understanding of these wide-area, fully distributed computing systems. After the advances made in distributed system design, collaborative environments, high performance computing and high throughput computing, the Grid is the logical next step.

The new Aims and Scope of FGCS will cover new developments in:

[1] Grid Applications and application support:

    Novel applications
    eScience and eBusiness applications
    Problem solving environments and virtual laboratories
    Grid economy
    Semantic and knowledge based grids
    Collaborative Grids and virtual organizations
    High Performance and high throughput computing on grids
    Complex application workflows
    Scientific, industrial and social implications
    Grids in education

[2] Grid methods and middleware:

    Tools for grid development: monitoring and scheduling
    Distributed dynamic resource management
    Grid- and web-services
    Information management
    Protocols and emerging standards
    Peer to peer and internet computing
    Pervasive computing
    Grid Security

[3] Grid Theory:

    Process specification; program and algorithm design
    Theoretical aspects of wide area communication and computation
    Scaling and performance theory
    Protocol verification

Special Issue on Scalable Compute Continuum
Submission Date: 2023-11-03

Motivation and Scope

The “Compute Continuum” paradigm promises to manage the heterogeneity and dynamism of widespread computing resources, aiming to simplify the execution of distributed applications improving data locality, performance, availability, adaptability, energy management as well as other non-functional features. This is made possible by overcoming the fragmentation of IoT-edge-cloud resources and their segregation in tiers, enabling applications to be seamlessly executed and relocated along a continuum of resources spanning from the edge to the cloud.
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By distributing resources all around, the emerging Compute Continuum paradigm supports the execution of data-intensive applications as close as possible to data sources and end users. Besides consolidated vertical and horizontal scaling patterns, this paradigm also offers more detailed adaptation actions that strictly depend on the specific infrastructure components (e.g., to reduce energy consumption, or to exploit specific hardware such as GPUs and FPGAs). This enables the enhancement of latency-sensitive applications, the reduction of network bandwidth consumption, the improvement of privacy protection, and the development of novel services aimed at improving living, health, safety, and mobility. All of this should be achievable by application developers without having to worry about how and where the developed application components will be executed. Therefore, to unleash the true potential offered by the Compute Continuum, autonomous, proactive, and infrastructure-aware management is desirable, if not mandatory, calling for novel interdisciplinary approaches that exploit optimization theory, control theory, machine learning, and artificial intelligence methods.

This special issue aims to investigate and gather research contributions on the emerging Compute Continuum, seeking solutions for running distributed applications while efficiently managing heterogeneous and widespread computing resources.

Guest Editors

Valeria Cardellini, University of Rome Tor Vergata, Italy. cardellini@ing.uniroma2.it
Patrizio Dazzi, University of Pisa, Italy. patrizio.dazzi@unipi.it
Gabriele Mencagli, University of Pisa, Italy. gabriele.mencagli@unipi.it
Matteo Nardelli, Bank of Italy, Italy. matteo.nardelli@bancaditalia.it 
Massimo Torquati, University of Pisa, Italy. massimo.torquati@unipi.it

Important Dates
Submission portal opens: May 1, 2023
Deadline for paper submission: November 3, 2023
Latest acceptance deadline for all papers: March 8, 2024

Special Issue on Edge-Cloud Solutions for Big Data Analysis and Distributed Machine Learning
Submission Date: 2023-11-30

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.
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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.
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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.

Guest Editors
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Important Dates
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Submission portal opens: May 20, 2023
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Deadline for paper submission: Nov 30, 2023
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Latest acceptance deadline for all papers: Feb 29, 2024 

Special Issue on Advanced Technologies in E-Business Engineering and Applications
Submission Date: 2024-02-15

Motivation and Scope

E-business engineering and applications are continuously changing due to the rapid development of state-of-the-art technologies such as big data, cloud, Internet of Things (IoT), and Artificial Intelligence (AI). Big data has a wider application in successfully provisioning E-business services, such as data analytics for customer engagement, increase in sales, and personalization of customer experience. IoT has been increasingly used in modern E-business for various purposes such as improving logistics and tracking processes, automating shipping and delivery, and maximizing time and profit. However, given big data's large volume, variety, and velocity, it must be processed before its intended benefits can be achieved. This ranges from sufficient data storage to ensure the processed analysis is understood and free from bias. Businesses and organizations, therefore, need to adopt cloud-enabled elastic resources and sophisticated tools that will assist the AI models in the decision-making process. This also ensures that businesses comply with the General Data Protection Regulation (GDPR) which requires them to explain to their consumers how their AI-based decision models have led them to the decision being reached.

This special issue aims to explore further new research in the advanced technologies of big data, cloud, Internet, IoT, and their AI applications to advance the area of E-business systems and applications. This special issue invites papers targeting the following topics:

Big data models and technologies
Big data analytics and visualization
Cloud computing and big data in E-business
Cloud and IoT in E-Business
Big data and NoSQL databases
Big data and knowledge engineering
E-business data mining and data extraction
Machine learning and big data in E-business
Security, privacy, and trust in E-business
AI-based models for decision-making in business
Reliability and trustworthiness of the AI-based decision-making models
AI and big data in business applications