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1. Subject-Related Research Metrics in Different Scientometrics Platforms.

Author

Khorasani, Mahyar

Subjects
DATABASES, COMPUTER software, MEDICAL information storage & retrieval systems, ACCOUNTING, BIBLIOMETRICS, SERIAL publications, QUANTITATIVE research, PSYCHOLOGY, CITATION analysis, MATHEMATICS, ECONOMICS, HUMANITIES, SCIENCE, AUTHORSHIP, ALGORITHMS
Abstract

This article investigates subject-related research metrics from the most popular scientometrics platforms with the capability of citation count. Quantitative analyses have been carried out to determine the most- to the least-cited subject areas to indicate the most active and dynamic fields in research. Subject areas are classified based on statistical analyses and the number of citations received in the period of search. Numerous articles were analyzed from top-ranked journals in all research fields covered by SCImago journal classification. A comprehensive data set was generated in 27 research subject areas, including 313 subject categories and 81 high-ranked journals. Results showed that the highest number of citations were related to the sciences and science-related subject areas. The subject areas of computer science and mathematics had the lowest research metrics and the smallest number of highly cited publications. Web of Science performed better than Scopus for subject-related evaluations. Analysis of frequency data showed that Google Scholar cited fewer papers for low-cited publications than did Scopus or Web of Science. For highly cited publications, however, Google Scholar was found to have better performance. Web of Science showed the best consistency in citation coverage in most of the investigated subject areas. [ABSTRACT FROM AUTHOR]

Published
2022
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2. Dynamic performance–Energy tradeoff consolidation with contention-aware resource provisioning in containerized clouds

Author

Rewer M. Canosa-Reyes, Andrei Tchernykh, Jorge M. Cortés-Mendoza, Bernardo Pulido-Gaytan, Raúl Rivera-Rodriguez, Jose E. Lozano-Rizk, Eduardo R. Concepción-Morales, Harold Enrique Castro Barrera, Carlos J. Barrios-Hernandez, Favio Medrano-Jaimes, Arutyun Avetisyan, Mikhail Babenko, and Alexander Yu. Drozdov

Subjects
Employment, European People, Computer and Information Sciences, Economics, Science Policy, Imaging Techniques, Science, Social Sciences, Neuroimaging, Jobs, Research and Analysis Methods, Computer Architecture, Diagnostic Radiology, Computer Software, Open Science, Diagnostic Medicine, Medicine and Health Sciences, Ethnicities, Tomography, Multidisciplinary, Applied Mathematics, Simulation and Modeling, Radiology and Imaging, Software Engineering, Biology and Life Sciences, Cloud Computing, Computer Hardware, Computing Methods, Computed Axial Tomography, Labor Economics, People and Places, Physical Sciences, Medicine, Engineering and Technology, Population Groupings, Mathematics, Algorithms, Open Source Software, Research Article, Slavic People, Neuroscience
Abstract

Containers have emerged as a more portable and efficient solution than virtual machines for cloud infrastructure providing both a flexible way to build and deploy applications. The quality of service, security, performance, energy consumption, among others, are essential aspects of their deployment, management, and orchestration. Inappropriate resource allocation can lead to resource contention, entailing reduced performance, poor energy efficiency, and other potentially damaging effects. In this paper, we present a set of online job allocation strategies to optimize quality of service, energy savings, and completion time, considering contention for shared on-chip resources. We consider the job allocation as the multilevel dynamic bin-packing problem that provides a lightweight runtime solution that minimizes contention and energy consumption while maximizing utilization. The proposed strategies are based on two and three levels of scheduling policies with container selection, capacity distribution, and contention-aware allocation. The energy model considers joint execution of applications of different types on shared resources generalized by the job concentration paradigm. We provide an experimental analysis of eighty-six scheduling heuristics with scientific workloads of memory and CPU-intensive jobs. The proposed techniques outperform classical solutions in terms of quality of service, energy savings, and completion time by 21.73–43.44%, 44.06–92.11%, and 16.38–24.17%, respectively, leading to a cost-efficient resource allocation for cloud infrastructures.

Published
2022