Scalable Computing universitatea de vest Journal Impact Factor

Scalable Computing - universitatea de vest | 2024 Impact Factor:3.5 | Cite Score:1.0 | Q3

Scalable Computing Journal With Cite Score

Cite Score and Journal Rank of Scalable Computing

About: The Scalable Computing journal is a peer-reviewed academic publication dedicated to research in scalable computing systems and methodologies. It focuses on the design, implementation, and performance evaluation of computing systems that can efficiently handle large-scale data and computational tasks. The journal covers a broad range of topics related to scalable computing, including distributed systems, parallel computing, cloud computing, and high-performance computing.
Objective:
The primary objective of Scalable Computing is to advance the field of scalable computing by publishing high-quality research that addresses the challenges and opportunities associated with large-scale computing systems. The journal aims to provide a platform for researchers, practitioners, and developers to share innovative solutions, methodologies, and technologies that contribute to the effective management and utilization of scalable computing resources.
Interdisciplinary Approach:
Scalable Computing adopts an interdisciplinary approach by integrating research from various domains related to scalable computing. This includes areas such as computer science, engineering, data science, and applied mathematics. The journal encourages contributions that bridge these disciplines to develop novel approaches and solutions for scalable computing challenges, promoting cross-disciplinary collaboration and innovation.
Impact:
The journal has a significant impact on both the academic community and industry practitioners involved in scalable computing. Scalable Computing is widely cited for its contributions to the development of new technologies, methodologies, and performance evaluation techniques for large-scale computing systems. The research published in the journal drives advancements in the field and informs the development of next-generation scalable computing solutions.
Significance:
Scalable Computing plays a crucial role in advancing the understanding and application of scalable computing systems. By providing a platform for high-quality research and innovative approaches, the journal supports the development of effective and efficient computing solutions for large-scale data and computational tasks. Its focus on interdisciplinary research and practical applications makes it a valuable resource for scholars, practitioners, and industry professionals seeking to address the complexities of scalable computing systems.

Journal Home: Journal Homepage

Editor-in-Chief: Dana Petcu

Scope: The Scalable Computing: Practice and Experience journal focuses on scalable computing solutions and practices across a range of applications and technologies. It addresses the challenges and advancements in creating and managing scalable computing systems. The journal covers theoretical, practical, and experimental research that enhances the scalability and performance of computing systems. Its scope includes, but is not limited to, the following areas:
Scalable Algorithms: Development and analysis of algorithms designed for scalable computing environments, including parallel algorithms, distributed algorithms, and scalable data structures.
High-Performance Computing (HPC): Research on HPC systems and architectures, including supercomputing, parallel processing, cluster computing, and performance optimization techniques.
Cloud Computing: Studies on scalable cloud computing architectures and services, including infrastructure-as-a-service (IaaS), platform-as-a-service (PaaS), and software-as-a-service (SaaS), as well as cloud resource management and optimization.
Distributed Systems: Research on the design, implementation, and evaluation of distributed systems, including distributed databases, distributed file systems, and distributed computing models.
Grid Computing: Exploration of grid computing technologies and applications, including resource sharing, grid middleware, and grid-enabled applications.
Big Data and Analytics: Techniques and technologies for managing and analyzing large-scale data sets, including distributed data processing frameworks, data warehousing, and scalable data analytics.
Parallel Computing: Research on parallel computing techniques, including multi-core and many-core processors, parallel programming models, and parallel software development tools.
Scalable Networking: Studies on networking technologies and protocols for scalable systems, including network architecture, congestion control, and network performance optimization.
Performance Evaluation: Methods for evaluating the performance of scalable computing systems, including benchmarking, performance metrics, and performance tuning.
Scalable Storage Systems: Research on scalable storage solutions, including distributed storage systems, storage management, and data redundancy.
Embedded Systems: Application of scalable computing principles to embedded systems, including scalable architectures, real-time processing, and resource management.
Cyber-Physical Systems: Integration of scalable computing technologies with physical systems, including sensors, actuators, and control systems.
Software Scalability: Techniques for designing and implementing scalable software systems, including scalable software architectures, load balancing, and software optimization.
Energy Efficiency: Research on energy-efficient computing techniques and architectures for scalable systems, including energy-aware algorithms and power management.
Security and Privacy: Studies on security and privacy issues in scalable computing environments, including secure communication, data protection, and access control.
Applications and Case Studies: Practical applications and case studies that demonstrate the use of scalable computing solutions in various domains, including scientific computing, engineering, healthcare, and finance.