Who publishes Future Generation Computer Systems?

Future Generation Computer Systems is published by Elsevier.

What is the ISSN of Future Generation Computer Systems?

The ISSN of Future Generation Computer Systems is 0167-739X.

What is the impact factor of Future Generation Computer Systems?

The reported impact factor of Future Generation Computer Systems is 5.9. Impact factor values are reproduced from publishers and may lag the latest JCR release.

What is the CCF ranking of Future Generation Computer Systems?

Future Generation Computer Systems is ranked CCF C in the China Computer Federation journal catalogue.

期刊信息

Future Generation Computer Systems (FGCS)

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影响因子:

5.9

出版商:

Elsevier

ISSN:

0167-739X

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177727

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征稿

Future Generation Computer Systems (FGCS) is an academic journal published by Elsevier. (ISSN 0167-739X, impact factor 5.9, CCF C).

Aims & Scope The International Journal of eScience Computing infrastructures and systems are rapidly developing and so are novel ways to map, control and execute scientific applications which become more and more complex and collaborative. Computational and storage capabilities, databases, sensors, and people need true collaborative tools. Over the last years there has been a real explosion of new theory and technological progress supporting a better understanding of these wide-area, fully distributed sensing and computing systems. Big Data in all its guises require novel methods and infrastructures to register, analyze and distill meaning. FGCS aims to lead the way in advances in distributed systems, collaborative environments, high performance and high performance computing, Big Data on such infrastructures as grids, clouds and the Internet of Things (IoT). The Aims and Scope of FGCS cover new developments in: [1] Applications and application support: Novel applications for novel e-infrastructures Complex workflow applications Big Data registration, processing and analyses Problem solving environments and virtual laboratories Semantic and knowledge based systems Collaborative infrastructures and virtual organizations Methods for high performance and high throughput computing Urgent computing Scientific, industrial, social and educational implications Education [2] Methods and tools: Tools for infrastructure development and monitoring Distributed dynamic resource management and scheduling Information management Protocols and emerging standards Methods and tools for internet computing Security aspects [3] Theory: Process specification; Program and algorithm design Theoretical aspects of large scale communication and computation Scaling and performance theory Protocols and their verification

由 Admin Agent 最后更新于 2026-08-12

Special Issues

Special Issue on Powering Data-driven Innovation: Responsible, Sustainable, Scalable, and Trustworthy AI for the Future 截稿日期: 2026-10-31 Motivation and Scope: As the 4th digital revolution has not entered into full force yet, we are already moving into the 5th revolution, driven by Artificial Intelligence (AI), with the aim of promoting new, efficient, and resilient digital systems. At the heart of this shift are vast amounts of data and enabling technologies that require high-performance computing (HPC) to extract insights in a sustainable way. However, challenges remain. Data processes must align with legal and ethical standards, such as the EU’s Digital Services and Data Governance Acts. To navigate this complex landscape, we need data science practices that are not only scalable, responsible, and sustainable, but that also drive AI systems toward greater efficiency than traditional approaches, positioning them as enablers of sustainability rather than contributors to the problem. Together, these advances lay the foundation for a powerful yet ethical and sustainable digital future. This special issue aims to explore the intersection of data science and HPC within the framework of responsible and trustworthy AI, seeking contributions that address the challenges and opportunities in developing data-driven, human-centric, and energy-efficient AI solutions at scale. The topics of this special issue include (but are not limited to): Scalable data science methodologies for large, complex, and distributed datasets; Innovations in federated and privacy-preserving data analysis; Integration of HPC with modern data science pipelines; Tools, frameworks, and methodologies for responsible, trustworthy, and explainable data-driven modeling and pipelines; Real-world applications demonstrating the impact of scalable and distributed data science; Governance, reproducibility, and ethical considerations in decentralized data environments; Novel case studies of data-driven AI and HPC. Guest Editors: Alessia Antelmi, University of Turin, Italy, [email protected] Claudio Ardagna, University of Milan, Italy, [email protected] Nicola Bena, University of Milan, Italy, [email protected] Massimo Cafaro, University of Salento, Italy, [email protected] Roberto Esposito, University of Turin, Italy, [email protected] Mirko Polato, University of Turin, Italy, [email protected] Important Dates: Submission portal opens: May 1, 2026 Deadline for paper submission: Oct 31, 2026 Latest acceptance deadline for all papers: Feb 28, 2027 Manuscript Submission Instructions: The FGCS’s submission system Editorial Manager® will be open for submissions to our Special Issue from May 1, 2026. When submitting your manuscript, please select the article type VSI: Powering Data-driven Innovation. 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. Keywords: high-performance computing, distributed and parallel computing, big data analytics, energy efficient computing, machine learning applications, sustainable AI, responsible AI, federated learning, privacy-preserving data analysis https://www.sciencedirect.com/special-issue/331744/powering-data-driven-innovation-responsible-sustainable-scalable-and-trustworthy-ai-for-the-future

由 Admin Agent 最后更新于 2026-08-12

Special Issue on Lightweight and Sustainable Computing across the Cloud-Edge Continuum 截稿日期: 2026-11-07 Computing infrastructures are evolving from centralized cloud deployments toward a heterogeneous continuum spanning cloud, edge, and extreme-edge resources. This shift introduces fundamental challenges in efficiency, adaptability, and sustainability that traditional system designs struggle to address. This Special Issue on Lightweight and Sustainable Computing across the Cloud-Edge Continuum solicits high-quality research on the foundational system layers of this emerging infrastructure. Topics include lightweight execution environments and runtimes, orchestration and scheduling mechanisms, sustainability-aware resource management, support for AI and agentic workloads, and benchmarking methodologies. We welcome contributions advancing the design of adaptive, efficient, and sustainable distributed systems across heterogeneous continuum infrastructures. Guest editors: Luiz Bittencourt, University of Campinas, Brazil, [email protected] Valeria Cardellini, University of Rome Tor Vergata, Italy, [email protected] Aaron Ding, Delft University of Technology, The Netherlands, [email protected] Matteo Nardelli, Bank of Italy, Italy, [email protected] Gabriele Russo Russo, Tor Vergata University of Rome, Italy, [email protected] Special issue information: The cloud-edge compute continuum is changing the execution environment of distributed systems. Computing resources are no longer deployed only into homogeneous and centrally managed infrastructures, but are increasingly diffused across heterogeneous, dynamic, and resource-constrained platforms spanning from the cloud to the extreme edge (potentially including Low-Earth orbit satellite components), aiming to sustain the ever-growing computing demand of pervasive applications and services. While this evolution expands the design space for distributed applications, it also exposes fundamental challenges related to efficiency, adaptability, scalability, security, and sustainability. Traditional assumptions on deployment, execution, and resource management become increasingly fragile. Static provisioning models, heavyweight execution environments, and centralized control mechanisms struggle to cope with the variability of network conditions, hardware capabilities, workload dynamics, and energy constraints that characterize cloud-edge continuum infrastructures. As a result, ensuring predictable performance, efficient resource utilization, and robust operation across the continuum requires a rethinking of system abstractions and management strategies. A key challenge lies in enabling flexible execution and placement of computation across heterogeneous resources while preserving isolation, portability, and low overhead. This has stimulated growing interest in lightweight execution models and runtimes, such as unikernels or WebAssembly-based sandboxes, which aim to reduce the cost of instantiation and migration while supporting fine-grained control over resource usage. Additional complexity arises from the increasing importance of data-driven and AI-centric workloads, which often exhibit highly variable resource demands and strict latency requirements. The emergence of increasingly complex agentic workflows also calls for adaptive and resource-aware orchestration mechanisms and policies. Sustainability concerns further exacerbate these challenges, as energy consumption, carbon footprint, and responsible use of resources are becoming first-class objectives in distributed system design. Topics of interest include, but are not limited to:- scalable architectures and systems for the cloud-edge continuum; - lightweight and sustainable execution environments and runtimes; - system software for orchestration; - distributed resource management and scheduling; - runtime-orchestration-scheduling co-design; - programming abstractions for adaptive applications; - performance modeling and monitoring; - sustainability-aware and carbon-aware resource management; - support for data-driven and agentic AI workloads; - infrastructure heterogeneity including extreme-edge and satellite platforms; - benchmarks and reproducible evaluation methodologies. Important Dates Submission portal opens: June 01, 2026 Deadline for paper submission: November 07, 2026 Latest acceptance deadline for all paper: May 02, 2027 Manuscript submission information: The FGCS’s submission system (Editorial Manager®) will be open for submissions to our Special Issue fromJune 01, 2026. When submitting your manuscript, please select the article type VSI:Cloud-Edge Continuum. 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. Keywords: cloud-edge continuum; edge-cloud systems; heterogeneous distributed systems; resource management; lightweight runtimes; WebAssembly; adaptive orchestration; scheduling; sustainable computing; energy-aware systems https://www.sciencedirect.com/special-issue/334720/lightweight-and-sustainable-computing-across-the-cloud-edge-continuum

由 Admin Agent 最后更新于 2026-08-12

Special Issue on Novel Paradigms and Scalable Computational Frameworks for Data-Intensive Research in Computational Biology and Medical Informatics 截稿日期: 2027-01-03 This Special Issue explores novel paradigms and scalable computational frameworks for data-intensive research in computational biology and medical informatics. The rapid growth of clinical, molecular, and multi-omics data is creating unprecedented opportunities for discovery while simultaneously posing significant challenges for data processing, integration, and analysis. The contributions collected in this Special Issue focus on advanced computing solutions such as high-performance computing, distributed and GPU-accelerated architectures, AI-driven analytical workflows, and emerging paradigms including quantum and quantum-inspired computing. By connecting developments in computing systems with real-world life science applications, the Special Issue highlights methodologies and infrastructures that support the next generation of large-scale biomedical data analysis. Guest editors: Prof. Marco Beccuti, University of Turin, Turin, Italy, [email protected] Prof. Umberto Ferraro Petrillo, Sapienza University of Rome, Rome, Italy, [email protected] Dr. Lorenzo Di Rocco, Sapienza University of Rome, Rome, Italy, [email protected] Special issue information: The rapid growth of clinical, molecular, and omics data is increasingly driving new discoveries in biomedical and biological research. The combination of advanced computational infrastructures, such as GPU-accelerated and distributed computing systems, with machine learning and AI-driven workflows is accelerating the research process and enabling the extraction of meaningful insights from extensive datasets. Developing scalable, robust, and interpretable methods is essential to effectively manage the heterogeneity and complexity of modern large-scale datasets. As these data-intensive challenges become central to the fields of bioinformatics, biostatistics, and medical informatics, there is a growing demand for novel paradigms that effectively harness state-of-the-art learning techniques, as well as for engineering solutions that enable the efficient management of high-volume data. At the same time, engineering solutions capable of exploiting high-performance computing architectures and distributed environments are becoming increasingly important to support large-scale biomedical analysis pipelines. This Special Issue focuses on novel paradigms and scalable computational frameworks that leverages high-performance computing, distributed and GPU-accelerated architectures, as well as advanced machine learning and AI-based workflows, to address the challenges of modern data-intensive research in computational biology and medical informatics. Contributions exploring novel system architectures, scalable algorithms, integrated data processing pipelines, and emerging computing paradigms, including quantum and quantum-inspired approaches for complex data analysis, are particularly welcome. Important Dates Submission portal opens: October 5, 2026 Deadline for paper submission: February 1, 2027 Latest acceptance deadline for all paper: May 10, 2027 Manuscript submission information: The FGCS’s submission system (Editorial Manager®) will be open for submissions to our Special Issue from October 5, 2026. When submitting your manuscript, please select the article type VSI: SCF-CBMI. 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. Keywords: high-performance computing, distributed computing, GPU-accelerated computing, artificial intelligence, machine learning, computational biology, multi-omics data analysis, medical informatics, quantum computing https://www.sciencedirect.com/special-issue/334600/novel-paradigms-and-scalable-computational-frameworks-for-data-intensive-research-in-computational-biology-and-medical-informatics

由 Admin Agent 最后更新于 2026-08-12

Special Issue on Future Generation Information Technology for Social Good 截稿日期: 2027-02-28 Technological innovation is increasingly pivotal in addressing global societal challenges and advancing the UN Sustainable Development Goals (SDGs): in healthcare, smart cities, environmental monitoring, and digital governance, to name just a few areas. Emerging computing paradigms—such as distributed systems, artificial intelligence, and data-driven infrastructures—are bringing digital social innovation to a new level of inclusivity and sustainability. This requires a user-centered approach where technological design, advanced hardware, and societal needs are holistically considered. This special issue aims to explore the multifaceted aspects of future generation computing, highlighting both technical advances and their broader societal implications for social good. Guest editors: Barbara Guidi, [email protected], University of Pisa, Italy Pietro Manzoni, [email protected], Polytechnic Univ. of Valencia, Spain Claudio E. Palazzi, [email protected], University of Padua, Italy Special issue information: Technological innovation plays a pivotal role in advancing the UN Sustainable Development Goals (SDGs) and the “Agenda 2030.” Emerging computing paradigms—such as distributed systems, artificial intelligence, and data-driven infrastructures—offer unprecedented opportunities to design scalable and impactful solutions for social good. Digital social innovation sits at the intersection of technology, societal challenges, and environmental sustainability. It emphasizes digital technologies in enabling communities to share data, collaborate, and scale solutions through open, distributed, and participatory approaches. A key challenge lies in effectively integrating emerging technologies, including advanced hardware (e.g., IoT, wearables, TinyML), digital paradigms (e.g., AI, blockchain, big data), and communication infrastructures (e.g., 5G). Furthermore, future ICT solutions must ensure inclusiveness, accessibility, affordability, and sustainability. Addressing these aspects requires a strictly user-centered perspective. This Special Issue focuses on novel IT solutions enabling collaborative and sustainable applications across domains like healthcare, smart cities, environmental monitoring, and digital governance. It aims to provide an interdisciplinary forum for researchers to present innovative models leveraging future generation computing. We strongly encourage multidisciplinary contributions exploring the intersection of computer science with social sciences and public policy. The Special Issue is open to submissions from the wider research community and will also consider extended versions of selected papers from the ACM International Conference on Information Technology for Social Good (GoodIT), provided they align with the scope. Our goal is to foster technology-driven solutions that advance the state of the art in computing and contribute to a socially responsible digital future. Topics of interest include, but are not limited to: - AI, Digital Twins, and Blockchain for social good - Accessible and Inclusive Technologies, bridging the digital divide - Citizen Science, Civic Intelligence, and Social Informatics - Digital Democracy, open data, and disinformation management - Ethical Computing, privacy, and trust in ICT solutions - Frugal Solutions and Sustainable IT - Gamification and Serious Games for social impact - IT for Health, Social Care, Education, and Development - Smart Governance, Sustainable Cities, and Environmental Monitoring - Human-Computer and Human-Robot Interaction - Decentralized Environments, networking, and social media Important Dates Submission portal opens: September 30, 2026 Deadline for paper submission: February 28, 2027 Latest acceptance deadline for all paper: July 01, 2027 Manuscript submission information: The FGCS’s submission system (Editorial Manager®) will be open for submissions to our Special Issue from September 30, 2026. When submitting your manuscript, please select the article type VSI:FUTURE_Social Good. 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. Keywords: social good; digital social innovation; sustainable development goals; distributed systems; artificial intelligence; blockchain https://www.sciencedirect.com/special-issue/334718/future-generation-information-technology-for-social-good

由 Admin Agent 最后更新于 2026-08-12

Special Issue on Advances in Quantum Computing: Methods, Algorithms, and Systems. Vol. IV 截稿日期: 2027-04-30 Quantum computing is rapidly evolving, with quantum systems, requiring integration with HPC and QPUs-as-accelerators. This special Issue spotlights foundations and practice: large-scale simulators; quantum compilers, runtimes, workflow managers, schedulers, and orchestrators; quantum algorithms and applications (including QML, quantum chemistry, …); quantum data and memories; error-correction codes; hybrid QC-HPC methods; performance modeling and characterization; cloud-based quantum services; quantum technologies for computation; speed-up and supremacy assessment; and benchmarking. We welcome contributions that tackle hardware-software co-design, classical-quantum interfaces, and validation at scale, showcasing both current results and emerging directions. Guest editors: Stefano S. Markidis, [email protected], KTH Royal Institute of Technology, Sweden. Michela Taufer, [email protected], The University of Tennessee Knoxville, United States Lucio L. Grandinetti, [email protected], University of Calabria, Italy Special issue information: We welcome contributions that tackle hardware-software co-design, classical-quantum interfaces, and validation at scale, showcasing both current results and emerging directions. Important Dates Submission portal opens: June 26, 2026 Deadline for paper submission: April 30, 2027 Latest acceptance deadline for all paper: June 30, 2027 https://www.sciencedirect.com/special-issue/334993/special-collection-on-advances-in-quantum-computing-methods-algorithms-and-systems-vol-iv

由 Admin Agent 最后更新于 2026-08-12

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B	Computer Networks	4.7	Elsevier	1389-1286
B	Journal of Systems and Software	3.8	Elsevier	0164-1212
C	Engineering Applications of Artificial Intelligence	9.0	Elsevier	0952-1976
B	Journal of Parallel and Distributed Computing	3.4	Elsevier	0743-7315
B	The Journal of Systems Architecture: Embedded Software Design	5.3	Elsevier	1383-7621
	IEEE Security & Privacy	3.0	IEEE	1540-7993
C	Discover Computing	1.9	Springer	2948-2992
B	Information Sciences	6.0	Elsevier	0020-0255
C	Image and Vision Computing	5.0	Elsevier	0262-8856
	Applied Soft Computing	7.8	Elsevier	1568-4946

CCF	全称	影响因子	出版商	ISSN
	New Generation Computing	2.8	Springer	0288-3635
C	Knowledge-Based Systems	7.2	Elsevier	0950-7051
C	Neurocomputing	6.5	Elsevier	0925-2312
C	Pattern Recognition Letters	3.9	Elsevier	0167-8655
C	IEEE Transactions on Industrial Informatics	11.7	IEEE	1551-3203
C	IEEE Internet of Things Journal	8.9	IEEE	2327-4662
C	Engineering Applications of Artificial Intelligence	9.0	Elsevier	0952-1976
C	Expert Systems with Applications	7.5	Elsevier	0957-4174
C	IEEE Transactions on Big Data	5.7	IEEE	2332-7790
C	Computer Communications	4.3	Elsevier	0140-3664

CCF	ICORE	CP-I	简称	全称	截稿日期	会议日期
B	A	78.4	FPGA	International Symposium on Field-Programmable Gate Arrays	2026-10-01	2027-03-14
B	A	92.8	IPDPS	International Parallel & Distributed Processing Symposium	2026-10-01	2027-06-01
C	C	67.1	ISPA	International Symposium on Parallel and Distributed Processing with Applications	2026-09-30	2026-12-27
C	A	80.5	ECIR	European Conference on Information Retrieval	2026-09-21	2027-03-21
	B	59.3	ACIIDS	Asian Conference on Intelligent Information and Database Systems	2026-09-30	2027-04-05
	B	56.2	CAIN	International Conference on AI Engineering – Software Engineering for AI	2026-10-30	2027-04-25
	B	63.4	EvoApplications	International Conference on the Applications of Evolutionary Computation	2026-11-01	2027-03-31
	A	67.4	LAK	International Learning Analytics & Knowledge Conference	2026-09-28	2027-03-08
A	A*	89.3	ICLR	International Conference on Learning Representations	2026-09-18	2027-04-26
	C	45.6	HPC Asia	International Conference on High Performance Computing in Asia-Pacific Region	2026-09-13	2027-02-17

期刊信息

Future Generation Computer Systems (FGCS)

征稿

Special Issues

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