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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: 57 days
Avg accept to publ: 60 days
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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A Proposed Signal Reconstruction Algorithm over Bandlimited Channels for Wireless Communications, ASHOUR, A., KHALAF, A., HUSSEIN, A., HAMED, H., RAMADAN, A.
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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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  4/2021 - 11

A Security-Driven Approach for Energy-Aware Cloud Resource Pricing and Allocation

MIKAVICA, B. See more information about MIKAVICA, B. on SCOPUS See more information about MIKAVICA, B. on IEEExplore See more information about MIKAVICA, B. on Web of Science, KOSTIC-LJUBISAVLJEVIC, A. See more information about KOSTIC-LJUBISAVLJEVIC, A. on SCOPUS See more information about KOSTIC-LJUBISAVLJEVIC, A. on SCOPUS See more information about KOSTIC-LJUBISAVLJEVIC, A. on Web of Science
 
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Download PDF pdficon (2,003 KB) | Citation | Downloads: 784 | Views: 1,787

Author keywords
decision making, energy consumption, security, simulation, virtual machining

References keywords
cloud(29), comput(13), energy(12), computing(10), security(9), resource(9), data(8), auction(8), virtual(6), centers(6)
Blue keywords are present in both the references section and the paper title.

About this article
Date of Publication: 2021-11-30
Volume 21, Issue 4, Year 2021, On page(s): 99 - 106
ISSN: 1582-7445, e-ISSN: 1844-7600
Digital Object Identifier: 10.4316/AECE.2021.04011
Web of Science Accession Number: 000725107100011
SCOPUS ID: 85122259459

Abstract
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Auctions are often recommended as effective cloud resource pricing and allocation mechanism. If adequately set, auctions provide incentives for cloud users truthful bidding and support cloud providers revenue maximization. In such a cloud system, resources are offered via an auction mechanism as Virtual Machines (VMs). Due to the virtualization of the cloud system, VMs security becomes a critical factor. However, security requirements are often in contrast with performance requirements since the operation of security mechanism inevitably consumes a certain amount of Central Processing Time (CPU) and memory. Thus, delays and energy consumption increase. In this paper, we propose a novel simulation model based on a truthful auction mechanism to address revenues, security, and energy consumption in a cloud system. The VMs security modeling is introduced to assess the security level of VMs. A Vickrey-Clarke-Groves (VCG) driven algorithm is established for winner determination. The proposed simulation model is used to observe cloud providers revenues, lost revenues, cloud users' task rejection rate and energy consumption depending on the offered security level. This model supports decision making in terms of investments in security and selection of security scenario that maximizes revenues and minimizes task rejection rate and energy consumption.


References | Cited By

Cited-By Clarivate Web of Science

Web of Science® Times Cited: 3 [View]
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Cited-By SCOPUS

SCOPUS® Times Cited: 4
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Cited-By CrossRef

[1] Deadline-Aware Task Offloading and Resource Allocation in a Secure Fog-Cloud Environment, Mikavica, Branka, Kostic-Ljubisavljevic, Aleksandra, Perakovic, Dragan, Cvitic, Ivan, Mobile Networks and Applications, ISSN 1383-469X, Issue 1, Volume 29, 2024.
Digital Object Identifier: 10.1007/s11036-023-02120-y
[CrossRef]

[2] Digital twin-enabled hybrid deep evolutionary framework for smart building sustainable infrastructure management, Xu, Yunbo, Zhang, Jiachao, Qin, Heyu, Zhou, Hao, Yang, Zikai, Sustainable Energy Technologies and Assessments, ISSN 2213-1388, Issue , 2024.
Digital Object Identifier: 10.1016/j.seta.2024.103773
[CrossRef]

Updated 2 days ago

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


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