Parallel and Distributed Systems

A special issue of Future Internet (ISSN 1999-5903). This special issue belongs to the section "Network Virtualization and Edge/Fog Computing".

Deadline for manuscript submissions: 10 March 2026 | Viewed by 2955

Special Issue Editors


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Guest Editor
Institute of Parallel and Distributed Systems, SEIEE, Shanghai Jiao Tong University, Shanghai 200240, China
Interests: distributed systems; operating systems

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Guest Editor
1. Department of Engineering for Innovation, University of Salento, 73100 Lecce, Italy
2. Euro-Mediterranean Centre on Climate Change, Foundation, 73100 Lecce, Italy
Interests: parallel; distributed; grid/cloud/P2P computing; data mining; machine learning; deep learning; security and cryptography
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Special Issue Information

Dear Colleagues,

Thanks to the scalability provided by parallel and distributed systems, they have become the foundational building blocks in daily computing services. Nevertheless, recent years have seen significant workloads and hardware shifts in traditional distributed systems, which calls for new system designs. First, AI workloads represented by large language model training and serving have emerged, which require substantial computational power that can be provided by a parallel system. Second, the hardware infrastructure for parallel systems has become more heterogeneous with the emergence of accelerators like GPUs.

The aim of this Special Issue is to provide an overview of the latest developments in parallel and distributed systems. Both theoretical and practical aspects are of interest.

Topics of interest include, but are not limited to, the following:

  • Parallel and distributed systems for large language models (both training and inference);
  • Parallel and distributed systems under the heterogeneous hardware (both accelerators, e.g., GPU, or networks, e.g., NVLink and RDMA);
  • Data management in parallel and distributed systems;
  • Methods for building distributed and parallel infrastructure (e.g., both hardware and clusters);
  • Debug or verify the correctness of parallel systems;
  • Efficient and scalable distributed systems;
  • Reliable parallel and distributed systems;
  • Experiences building and benchmarking parallel and distributed systems;
  • Methods for automatic parallelism for programs.

Dr. Xingda Wei
Prof. Dr. Massimo Cafaro
Guest Editors

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Keywords

  • parallel and distributed systems
  • reliability
  • heterogeneous computing
  • scalability
  • benchmarking

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Published Papers (3 papers)

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Research

22 pages, 2004 KB  
Article
Edge–Cloud Platform for Monitoring Sustainability Metrics in Organizational Contexts
by Maurizio Cellura, Ylenia Galluzzo, Domenico Garlisi, Fabrizio Giuliano, Domenico Panno and Simona E. Rombo
Future Internet 2026, 18(1), 28; https://doi.org/10.3390/fi18010028 - 4 Jan 2026
Viewed by 259
Abstract
In the last few years, great attention has been turned towards global sustainability and related key needs. The ambitious United Nations Sustainable Development Goals (SDGs) have been introduced to address the main global challenges on this topic. However, sustainability challenges are highly interconnected [...] Read more.
In the last few years, great attention has been turned towards global sustainability and related key needs. The ambitious United Nations Sustainable Development Goals (SDGs) have been introduced to address the main global challenges on this topic. However, sustainability challenges are highly interconnected and often addressed in isolation, underscoring the need for integrated data-driven approaches. This paper presents the design of a data management software platform tailored for edge–cloud computing environments to support continuous SDG monitoring. The system enables real-time data collection at distributed edge devices and centralized analysis in the cloud, facilitating actionable insights in response to user-defined sustainability queries. As a practical demonstration, we describe the implementation of a module for monitoring energy consumption with reference to a given organization (e.g., a university). Full article
(This article belongs to the Special Issue Parallel and Distributed Systems)
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17 pages, 756 KB  
Article
A DLT-Aware Performance Evaluation Framework for Virtual-Core Speedup Modeling
by Zile Xiang and Thomas G. Robertazzi
Future Internet 2025, 17(11), 519; https://doi.org/10.3390/fi17110519 - 14 Nov 2025
Viewed by 555
Abstract
Scheduling computing is a well-studied area focused on improving task execution by reducing processing time and increasing system efficiency. Divisible Load Theory (DLT) provides a structured analytical framework for distributing partitionable computational and communicational loads across processors, and its adaptability has allowed researchers [...] Read more.
Scheduling computing is a well-studied area focused on improving task execution by reducing processing time and increasing system efficiency. Divisible Load Theory (DLT) provides a structured analytical framework for distributing partitionable computational and communicational loads across processors, and its adaptability has allowed researchers to integrate it with other models and modern technologies. Building on this foundation, previous studies have shown that Amdahl-like laws can be effectively combined with DLT to produce more realistic performance models. This paper further develops analytical models that further extend such integration by incorporating Gustafson’s Law and Juurlink’s Law into DLT to capture broader scaling behaviors. It also extends the analysis to workload distribution in virtual multicore systems, providing a more structured basis for evaluating parallel performance. Methods include analytically computing speedup as a function of the number of cores and the parallelizable fraction under different scheduling strategies, with comparisons across workload conditions. Results show that combining DLT with speedup laws and virtual core design offers a deeper and more structured approach for analytical parallel system evaluation. While the analysis remains theoretical, the proposed framework establishes a mathematical foundation for future empirical validation, heterogeneous workload modeling, and sensitivity analysis. Full article
(This article belongs to the Special Issue Parallel and Distributed Systems)
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36 pages, 2702 KB  
Article
Multi-Criteria Genetic Algorithm for Optimizing Distributed Computing Systems in Neural Network Synthesis
by Valeriya V. Tynchenko, Ivan Malashin, Sergei O. Kurashkin, Vadim Tynchenko, Andrei Gantimurov, Vladimir Nelyub and Aleksei Borodulin
Future Internet 2025, 17(5), 215; https://doi.org/10.3390/fi17050215 - 13 May 2025
Cited by 1 | Viewed by 1605
Abstract
Artificial neural networks (ANNs) are increasingly effective in addressing complex scientific and technological challenges. However, challenges persist in synthesizing neural network models and defining their structural parameters. This study investigates the use of parallel evolutionary algorithms on distributed computing systems (DCSs) to optimize [...] Read more.
Artificial neural networks (ANNs) are increasingly effective in addressing complex scientific and technological challenges. However, challenges persist in synthesizing neural network models and defining their structural parameters. This study investigates the use of parallel evolutionary algorithms on distributed computing systems (DCSs) to optimize energy consumption and computational time. New mathematical models for DCS performance and reliability are proposed, based on a mass service system framework, along with a multi-criteria optimization model designed for resource-intensive computational problems. This model employs a multi-criteria GA to generate a diverse set of Pareto-optimal solutions. Additionally, a decision-support system is developed, incorporating the multi-criteria GA, allowing for customization of the genetic algorithm (GA) and the construction of specialized ANNs for specific problem domains. The application of the decision-support system (DSS) demonstrated performance of 1220.745 TFLOPS and an availability factor of 99.03%. These findings highlight the potential of the proposed DCS framework to enhance computational efficiency in relevant applications. Full article
(This article belongs to the Special Issue Parallel and Distributed Systems)
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