International Journal of Grid and High Performance Computing

Scope & Guideline

Unlocking the Power of Grid and High-Performance Computing

Introduction

Delve into the academic richness of International Journal of Grid and High Performance Computing with our guidelines, detailing its aims and scope. Our resource identifies emerging and trending topics paving the way for new academic progress. We also provide insights into declining or waning topics, helping you stay informed about changing research landscapes. Evaluate highly cited topics and recent publications within these guidelines to align your work with influential scholarly trends.
LanguageEnglish
ISSN1938-0259
PublisherIGI GLOBAL
Support Open AccessNo
CountryUnited States
TypeJournal
Convergefrom 2009 to 2024
AbbreviationINT J GRID HIGH PERF / Int. J. Grid High Perform. Comput.
Frequency4 issues/year
Time To First Decision-
Time To Acceptance-
Acceptance Rate-
Home Page-
Address701 E CHOCOLATE AVE, STE 200, HERSHEY, PA 17033-1240

Aims and Scopes

The International Journal of Grid and High Performance Computing focuses on advancing the understanding and application of high-performance computing and grid technologies. It aims to bridge theoretical research with practical implementations across various domains.
  1. High-Performance Computing (HPC):
    The journal emphasizes the development of algorithms, architectures, and applications that leverage high-performance computing systems for complex computation tasks across various fields.
  2. Grid Computing Technologies:
    It explores the design and implementation of grid computing systems that facilitate resource sharing and collaboration among distributed systems.
  3. Cloud Computing Solutions:
    The journal covers innovative methodologies and frameworks for cloud computing, including resource management, security, and optimization strategies.
  4. Internet of Things (IoT) Applications:
    Research on IoT technologies and their integration with cloud and grid computing, focusing on real-time data processing and analytics.
  5. Security and Privacy in Computing Environments:
    It investigates security measures and protocols essential for safeguarding data and applications in cloud and grid infrastructures.
  6. Data Analysis and Machine Learning Techniques:
    The journal promotes research on big data analytics, machine learning applications, and their impact on improving performance and decision-making.
The International Journal of Grid and High Performance Computing has identified several trending and emerging themes in its recent publications. These themes reflect the evolving landscape of computing and the increasing complexity of applications and technologies.
  1. Artificial Intelligence and Machine Learning:
    There is a growing emphasis on the application of AI and machine learning techniques across various domains, particularly in data classification, security detection, and health monitoring.
  2. Energy Efficiency in Computing Systems:
    Research focused on energy-efficient designs and algorithms for computing systems is gaining traction, reflecting the industry's need for sustainable and eco-friendly computing solutions.
  3. Edge and Fog Computing:
    The emergence of edge and fog computing paradigms is highlighted, as they provide solutions for real-time data processing and resource management closer to data sources.
  4. Big Data Analytics:
    The application of big data analytics methodologies is increasingly prominent, with research focusing on harnessing large datasets for improved decision-making and operational efficiency.
  5. Security Frameworks for Cloud and IoT:
    There is a heightened focus on developing comprehensive security frameworks to protect cloud services and IoT devices against evolving threats and vulnerabilities.

Declining or Waning

Over recent years, certain themes within the International Journal of Grid and High Performance Computing have shown a decline in frequency or relevance. These waning scopes may reflect shifts in research focus or diminishing interest in specific methodologies.
  1. Traditional Networking Protocols:
    There has been a noticeable decline in papers focusing on traditional networking protocols, suggesting a shift towards more advanced and integrated approaches in networking technology.
  2. Basic Resource Allocation Strategies:
    Simple resource allocation methods seem to be less frequently addressed, indicating a trend towards more sophisticated, adaptive, and intelligent resource management solutions.
  3. Static Data Processing Models:
    The focus on static models for data processing has decreased, as researchers increasingly favor dynamic and real-time processing techniques that align with the needs of modern computing environments.
  4. Legacy Systems and Technologies:
    Research related to legacy systems appears to be waning, possibly due to the industry's move towards more modern, scalable, and flexible computing solutions.

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