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 353

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 (1 paper)

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36 pages, 2702 KiB  
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
Viewed by 107
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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