Parallel, Distributed and Grid/Cloud/P2P Computing

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: closed (30 June 2022) | Viewed by 6740

Special Issue Editors


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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, 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,

Recent years have witnessed a growing interest in parallel and distributed computing not just for large-scale processing of big data, but also in the context of several research trends, such as Artificial Intelligence and machine learning, data mining, wireless sensor networks, Internet of Things, green computing in data centers, etc. The aim of the Special Issue is to address current research and trends in the field by focusing on theory, technologies, and applications, highlighting both challenges and opportunities. The scope of the Special Issue is very broad, taking into account the interdisciplinarity of parallel and distributed computing. Among the topics of interest, though not exclusively, there are:

  • Parallel algorithms;
  • Distributed algorithms, including P2P algorithms;
  • Fault-tolerant algorithms;
  • Cloud and grid computing;
  • Architectures for parallel and distributed computing;
  • Green computing and data centers;
  • Middleware and libraries for parallel and distributed computing;
  • Scheduling and resource allocation.

Prof. Dr. Massimo Cafaro
Dr. Italo Epicoco
Dr. Marco Pulimeno
Guest Editors

Manuscript Submission Information

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Keywords

  • parallel computing
  • distributed computing
  • grid computing
  • cloud computing
  • P2P

Published Papers (2 papers)

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13 pages, 892 KiB  
Article
Globally Scheduling Volunteer Computing
by David P. Anderson
Future Internet 2021, 13(9), 229; https://doi.org/10.3390/fi13090229 - 31 Aug 2021
Cited by 2 | Viewed by 3476
Abstract
Volunteer computing uses millions of consumer computing devices (desktop and laptop computers, tablets, phones, appliances, and cars) to do high-throughput scientific computing. It can provide Exa-scale capacity, and it is a scalable and sustainable alternative to data-center computing. Currently, about 30 science projects [...] Read more.
Volunteer computing uses millions of consumer computing devices (desktop and laptop computers, tablets, phones, appliances, and cars) to do high-throughput scientific computing. It can provide Exa-scale capacity, and it is a scalable and sustainable alternative to data-center computing. Currently, about 30 science projects use volunteer computing in areas ranging from biomedicine to cosmology. Each project has application programs with particular hardware and software requirements (memory, GPUs, VM support, and so on). Each volunteered device has specific hardware and software capabilities, and each device owner has preferences for which science areas they want to support. This leads to a scheduling problem: how to dynamically assign devices to projects in a way that satisfies various constraints and that balances various goals. We describe the scheduling policy used in Science United, a global manager for volunteer computing. Full article
(This article belongs to the Special Issue Parallel, Distributed and Grid/Cloud/P2P Computing)
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21 pages, 885 KiB  
Article
A DFT-Based Running Time Prediction Algorithm for Web Queries
by Oscar Rojas, Veronica Gil-Costa and Mauricio Marin
Future Internet 2021, 13(8), 204; https://doi.org/10.3390/fi13080204 - 04 Aug 2021
Cited by 1 | Viewed by 1968
Abstract
Web search engines are built from components capable of processing large amounts of user queries per second in a distributed way. Among them, the index service computes the top-k documents that best match each incoming query by means of a document ranking [...] Read more.
Web search engines are built from components capable of processing large amounts of user queries per second in a distributed way. Among them, the index service computes the top-k documents that best match each incoming query by means of a document ranking operation. To achieve high performance, dynamic pruning techniques such as the WAND and BM-WAND algorithms are used to avoid fully processing all of the documents related to a query during the ranking operation. Additionally, the index service distributes the ranking operations among clusters of processors wherein in each processor multi-threading is applied to speed up query solution. In this scenario, a query running time prediction algorithm has practical applications in the efficient assignment of processors and threads to incoming queries. We propose a prediction algorithm for the WAND and BM-WAND algorithms. We experimentally show that our proposal is able to achieve accurate prediction results while significantly reducing execution time and memory consumption as compared against an alternative prediction algorithm. Our proposal applies the discrete Fourier transform (DFT) to represent key features affecting query running time whereas the resulting vectors are used to train a feed-forward neural network with back-propagation. Full article
(This article belongs to the Special Issue Parallel, Distributed and Grid/Cloud/P2P Computing)
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