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JCR Impact Factor: 0.700
JCR 5-Year IF: 0.700
SCOPUS CiteScore: 1.8
Issues per year: 4
Current issue: Aug 2024
Next issue: Nov 2024
Avg review time: 59 days
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APC: 300 EUR


PUBLISHER

Stefan cel Mare
University of Suceava
Faculty of Electrical Engineering and
Computer Science
13, Universitatii Street
Suceava - 720229
ROMANIA

Print ISSN: 1582-7445
Online ISSN: 1844-7600
WorldCat: 643243560
doi: 10.4316/AECE


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LATEST NEWS

2024-Jun-20
Clarivate Analytics published the InCites Journal Citations Report for 2023. The InCites JCR Impact Factor of Advances in Electrical and Computer Engineering is 0.700 (0.700 without Journal self-cites), and the InCites JCR 5-Year Impact Factor is 0.600.

2023-Jun-28
Clarivate Analytics published the InCites Journal Citations Report for 2022. The InCites JCR Impact Factor of Advances in Electrical and Computer Engineering is 0.800 (0.700 without Journal self-cites), and the InCites JCR 5-Year Impact Factor is 1.000.

2023-Jun-05
SCOPUS published the CiteScore for 2022, computed by using an improved methodology, counting the citations received in 2019-2022 and dividing the sum by the number of papers published in the same time frame. The CiteScore of Advances in Electrical and Computer Engineering for 2022 is 2.0. For "General Computer Science" we rank #134/233 and for "Electrical and Electronic Engineering" we rank #478/738.

2022-Jun-28
Clarivate Analytics published the InCites Journal Citations Report for 2021. The InCites JCR Impact Factor of Advances in Electrical and Computer Engineering is 0.825 (0.722 without Journal self-cites), and the InCites JCR 5-Year Impact Factor is 0.752.

2022-Jun-16
SCOPUS published the CiteScore for 2021, computed by using an improved methodology, counting the citations received in 2018-2021 and dividing the sum by the number of papers published in the same time frame. The CiteScore of Advances in Electrical and Computer Engineering for 2021 is 2.5, the same as for 2020 but better than all our previous results.

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  3/2024 - 3

Horizontal Scaling Implementation with Container Orchestrator. Reliability Analysis during Stress and Performance Testing

DINCA, A.-M. See more information about DINCA, A.-M. on SCOPUS See more information about DINCA, A.-M. on IEEExplore See more information about DINCA, A.-M. on Web of Science, AXINTE, S.-D. See more information about  AXINTE, S.-D. on SCOPUS See more information about  AXINTE, S.-D. on SCOPUS See more information about AXINTE, S.-D. on Web of Science, BACIVAROV, I. See more information about  BACIVAROV, I. on SCOPUS See more information about  BACIVAROV, I. on SCOPUS See more information about BACIVAROV, I. on Web of Science, PETRICA, G. See more information about PETRICA, G. on SCOPUS See more information about PETRICA, G. on SCOPUS See more information about PETRICA, G. on Web of Science
 
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Download PDF pdficon (2,334 KB) | Citation | Downloads: 296 | Views: 323

Author keywords
automated virtualization, container orchestrator, horizontal scaling, infrastructure reliability, performance indicators

References keywords
software(9), cloud(9), kubernetes(7), docker(6), computing(6), applications(5), technology(4), performance(4), orchestration(4), network(4)
Blue keywords are present in both the references section and the paper title.

About this article
Date of Publication: 2024-08-31
Volume 24, Issue 3, Year 2024, On page(s): 23 - 32
ISSN: 1582-7445, e-ISSN: 1844-7600
Digital Object Identifier: 10.4316/AECE.2024.03003
Web of Science Accession Number: 001306111400003
SCOPUS ID: 85202992366

Abstract
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Software platforms infrastructure encompasses the collection of physical resources used concomitantly for user interactions, computational or persistence operations. Virtualization facilitates dynamic allocation of resources to a platform, while staying within the infrastructure's limits, enabling deployments of multiple environments on a server, maximizing the computational power. This paper focuses on practical applications of virtualization principles on software platforms, and the analysis of performance indicators. Section II is centered on the Docker feature that packages the source code into images, facilitating testing in local containers, preparing them for the container orchestrator, Kubernetes that designs, creates and scales infrastructure components. The platform was scaled horizontally, because this method accommodates the applications technical requirements, as stated in Section V. To identify the underlying causes of an inefficient auto-scaling infrastructure, a Fishbone analysis was conducted, followed by a novel proposal of triggering scaling operations, using AI tools that decide when to rescale an infrastructure, instead of traditional alert levels. The simple and advanced health check mechanisms are studied and implemented in Section VI, through separate APIs that validate the systems status and dependencies automatically and periodically using Docker commands, proving the misleadingness of the container for the former, demonstrating the importance of such progressive mechanisms.


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Faculty of Electrical Engineering and Computer Science
Stefan cel Mare University of Suceava, Romania


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