About the Journal

International Journal of Cloud Computing (ISCSITR-IJCC) is a peer-reviewed, open-access journal dedicated to the advancement of cloud computing technology and its applications. The journal serves as a platform for researchers, industry professionals, and academics to share their latest findings, innovative approaches, and practical solutions in the rapidly evolving field of cloud computing. ISCSITR-IJCC publishes high-quality research that spans both theoretical and applied aspects of cloud computing, making it a key resource for the global research community.

Scope and Focus

ISCSITR-IJCC covers a wide range of topics related to cloud computing, including but not limited to:

  • Cloud Architecture and Infrastructure: Exploration of cloud computing models (IaaS, PaaS, SaaS), virtualization, and the design of scalable and resilient cloud infrastructures.
  • Cloud Security and Privacy: Addressing issues related to data security, encryption, identity management, and privacy in cloud environments.
  • Edge and Fog Computing: Research on the integration of edge and fog computing with cloud systems to enhance performance and minimize latency.
  • Big Data in the Cloud: Techniques for processing and analyzing large datasets in cloud environments, including distributed storage systems and data mining.
  • Cloud-based Internet of Things (IoT): Innovations in leveraging cloud platforms for IoT applications, real-time analytics, and remote management of devices.
  • Cloud Service Management: Optimizing cloud services, including cost management, resource allocation, performance monitoring, and service-level agreements (SLAs).
  • Hybrid and Multi-cloud Solutions: Exploration of architectures that combine public, private, and hybrid cloud strategies to meet diverse enterprise needs.
  • Energy Efficiency in Cloud Computing: Approaches for reducing the energy consumption and carbon footprint of data centers and cloud-based services.
  • AI and Machine Learning in the Cloud: Research on deploying AI and machine learning models in cloud environments, leveraging cloud resources for enhanced scalability.
  • Serverless Computing: Innovations in serverless architectures and function-as-a-service (FaaS) models, along with their impact on cloud computing efficiency.

Audience and Impact

ISCSITR-IJCC targets a broad readership that includes cloud computing researchers, software engineers, data scientists, IT professionals, and students. The journal's interdisciplinary approach makes it an essential resource for anyone involved in the development, implementation, or research of cloud technologies.

Submission and Review Process

ISCSITR-IJCC employs a double-blind peer-review process, ensuring that all submissions are evaluated based on their originality, technical quality, and contribution to the field. Authors benefit from constructive feedback provided by experts in cloud computing, which helps to refine and strengthen their research.

Open Access and Indexing

ISCSITR-IJCC follows an open-access model, making all published articles freely available to readers worldwide. This ensures that groundbreaking research is accessible to a broad audience, thereby fostering collaboration and innovation. The journal is indexed in major academic databases, which increases the visibility and citation of published work.

Special Issues and Conferences

In addition to its regular issues, ISCSITR-IJCC occasionally publishes special issues that focus on specific emerging trends or breakthrough developments in cloud computing. The journal also collaborates with international conferences, encouraging the submission of high-quality research papers presented at these events for inclusion in the journal.

Open Access and Indexing

ISCSITR-IJAI is committed to making research accessible to a global audience. The journal follows an open access model, ensuring that published articles are freely available to the public. This approach promotes the dissemination of knowledge and encourages collaboration across the AI research community. The journal is indexed in major academic databases, ensuring wide visibility and impact for published work.