Parallel, Distributed and Cloud Computing: Status, Prospects and Future

A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Computing and Artificial Intelligence".

Deadline for manuscript submissions: 20 June 2024 | Viewed by 1162

Special Issue Editors

Department of Mathematics “R. Caccioppoli”, University of Naples Federico II, 80126 Naples, Italy
Interests: cloud computing; high-performance computing; performance analysis; algorithms; parallel programming; parallel algorithms; scientific software; artificial intelligence; machine learning
Special Issues, Collections and Topics in MDPI journals
Department of Mathematics “R. Caccioppoli”, University of Naples Federico II, 80126 Naples, Italy
Interests: high-performance computing; performance analysis; energy-aware algorithms and systems; machine learning; parallelism in time; parallel programming; parallel algorithms; scalable algorithms; imaging; clustering; fault tolerance; scientific software; GP-GPU; databases

Special Issue Information

Dear Colleagues,

In the last decade, we have seen the rise of Grid and Cloud Computing environments realized through sophisticated middleware acting as operating systems that oversee the efficient management of resources. More recently, the Internet of Things and Edge Computing environments are aimed at making available in a transparent and friendly way the multitude of low power and heterogeneous resources available everywhere around us.

These environments are very different and require sophisticated programming models to achieve high performance with an ever-increasing focus on energy consumption.

The aim of this Issue is to collect the current and new trends in the theoretical, fundamental, and application research in High Performance, Parallel, Distributed and Cloud Computing, taking into account all the aspects that have emerged over time as crucial for systems durability, performance maintenance and increasing, energy sustainability and applications range expansion.

We invite researchers to submit their new results as well as reviews about theories, models, methodologies, technologies, algorithms and/or softwares, systems and architectures, in the field of Parallel, Distributed and Cloud Computing, as well as IoT, AI and Machine Learning, Edge Computing.

Prof. Dr. Giuliano Laccetti
Dr. Valeria. Mele
Guest Editors

Manuscript Submission Information

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Keywords

  • cloud computing
  • cloud systems
  • parallel computing
  • distributed computing
  • high-performance computing
  • parallel algorithms
  • parallel dynamical systems
  • GP-GPU
  • big data
  • artificial intelligence
  • machine learning
  • data mining
  • clustering
  • Internet of Things
  • parallelism in time
  • performance analysis
  • load balancing
  • distributed cloud
  • scalable algorithms
  • parallel systems
  • distributed systems
  • energy-aware algorithms and systems

Published Papers (1 paper)

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Research

15 pages, 447 KiB  
Article
Converting Concurrent Range Index Structure to Range Index Structure for Disaggregated Memory
by Bonmoo Koo, Jaesang Hwang, Jonghyeok Park and Wook-Hee Kim
Appl. Sci. 2023, 13(20), 11130; https://doi.org/10.3390/app132011130 - 10 Oct 2023
Viewed by 628
Abstract
In this work, we propose the Spread approach, which tailors a concurrent range index structure to a range index structure for disaggregated memory connected via RDMA (Remote Direct Memory Access). The Spread approach leverages the concept of tolerating transient inconsistencies in a concurrent [...] Read more.
In this work, we propose the Spread approach, which tailors a concurrent range index structure to a range index structure for disaggregated memory connected via RDMA (Remote Direct Memory Access). The Spread approach leverages the concept of tolerating transient inconsistencies in a concurrent range index structure to reduce the amount of expensive RDMA operations. Based on the Spread approach, we converted Blink-tree, a concurrent range index structure, to a range index structure for disaggregated memory called RF-tree. In our experimental study, RF-tree shows comparable performance to Sherman, a state-of-the-art and carefully crafted range index structure for disaggregated memory. Full article
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