JOURNAL OF PARALLEL AND DISTRIBUTED COMPUTING

Scope & Guideline

Advancing the Frontiers of Computing Innovation

Introduction

Welcome to the JOURNAL OF PARALLEL AND DISTRIBUTED COMPUTING information hub, where our guidelines provide a wealth of knowledge about the journal’s focus and academic contributions. This page includes an extensive look at the aims and scope of JOURNAL OF PARALLEL AND DISTRIBUTED COMPUTING, highlighting trending and emerging areas of study. We also examine declining topics to offer insight into academic interest shifts. Our curated list of highly cited topics and recent publications is part of our effort to guide scholars, using these guidelines to stay ahead in their research endeavors.
LanguageEnglish
ISSN0743-7315
PublisherACADEMIC PRESS INC ELSEVIER SCIENCE
Support Open AccessNo
CountryUnited States
TypeJournal
Convergefrom 1984 to 2024
AbbreviationJ PARALLEL DISTR COM / J. Parallel Distrib. Comput.
Frequency12 issues/year
Time To First Decision-
Time To Acceptance-
Acceptance Rate-
Home Page-
Address525 B ST, STE 1900, SAN DIEGO, CA 92101-4495

Aims and Scopes

The JOURNAL OF PARALLEL AND DISTRIBUTED COMPUTING focuses on advancing the fields of parallel and distributed computing through innovative research, methodologies, and applications. It aims to bring together a wide range of studies that address both theoretical and practical challenges in these domains.
  1. Parallel Computing Techniques:
    Research on algorithms and frameworks that enhance the performance and efficiency of parallel computing systems, including GPU acceleration, MPI, and OpenMP.
  2. Distributed Systems and Architectures:
    Studies on the design, implementation, and optimization of distributed systems, including cloud computing, edge computing, and decentralized platforms.
  3. Federated Learning and Machine Learning Applications:
    Exploring the integration of federated learning and machine learning techniques in distributed environments, particularly for privacy-preserving applications.
  4. Resource Management and Scheduling:
    Innovative approaches to resource allocation, task scheduling, and load balancing in heterogeneous and distributed computing environments.
  5. Network and Communication Protocols:
    Research on enhancing communication protocols and network architectures for efficient data transfer and reliability in distributed systems.
  6. Security and Privacy in Distributed Systems:
    Investigations into secure communication, data privacy, and resilient algorithms in the context of distributed computing.
  7. Big Data and High-Performance Computing:
    Studies that focus on the intersection of big data analytics and high-performance computing, addressing challenges in data processing and management.
The JOURNAL OF PARALLEL AND DISTRIBUTED COMPUTING is witnessing exciting developments in several emerging themes, reflecting the evolving landscape of technology and research priorities. This section outlines these trending areas.
  1. Federated Learning and Privacy-Preserving Techniques:
    There is a significant increase in research focusing on federated learning, particularly in its application to privacy-preserving data analysis in distributed environments, reflecting growing concerns over data security.
  2. Edge Computing Innovations:
    Research centered on edge computing continues to rise, emphasizing the need for decentralized processing and real-time analytics in IoT and mobile applications.
  3. AI and Machine Learning Integration:
    The integration of AI and machine learning in parallel and distributed systems is trending, with studies exploring how these technologies can enhance performance and efficiency.
  4. Blockchain Applications in Distributed Systems:
    An emerging theme is the application of blockchain technology to improve security and trust in distributed systems, particularly in cloud computing and IoT contexts.
  5. Energy-Efficient Computing:
    There is a growing focus on energy-efficient algorithms and architectures in distributed computing, driven by the need for sustainable computing solutions.
  6. Dynamic Resource Management and Scheduling:
    Innovations in adaptive resource management and scheduling algorithms are increasingly prominent, addressing the complexities of heterogeneous and dynamic environments.

Declining or Waning

While the JOURNAL OF PARALLEL AND DISTRIBUTED COMPUTING has consistently published impactful research, certain themes have shown signs of waning interest over the recent years. This section highlights those areas that are becoming less prominent.
  1. Traditional HPC without Integration of AI:
    Research focusing solely on traditional high-performance computing techniques without incorporating machine learning or AI elements has seen a decline, as the field shifts towards more integrated approaches.
  2. Basic Parallel Algorithms without Optimization Techniques:
    Studies presenting basic parallel algorithms without significant optimization or innovative enhancements are becoming less frequent, as researchers seek deeper insights and efficiencies.
  3. Legacy Distributed Systems:
    Research on older distributed systems technologies that do not adapt to modern demands, such as cloud-native architectures, is decreasing in favor of more contemporary approaches.
  4. General Surveys without Novel Contributions:
    Surveys that lack novel insights or contributions to the field are becoming less common, as journals increasingly prioritize original research that provides new findings or methodologies.

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