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JCR Impact Factor: 0.699
JCR 5-Year IF: 0.674
Issues per year: 4
Current issue: Nov 2018
Next issue: Feb 2019
Avg review time: 79 days


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

2018-Jun-27
Clarivate Analytics published the InCites Journal Citations Report for 2017. The JCR Impact Factor of Advances in Electrical and Computer Engineering is 0.699, and the JCR 5-Year Impact Factor is 0.674.

2017-Jun-14
Thomson Reuters published the Journal Citations Report for 2016. The JCR Impact Factor of Advances in Electrical and Computer Engineering is 0.595, and the JCR 5-Year Impact Factor is 0.661.

2017-Feb-16
With new technologies, such as mobile communications, internet of things, and wide applications of social media, organizations generate a huge volume of data, much faster than several years ago. Big data, characterized by high volume, diversity and velocity, increasingly drives decision making and is changing the landscape of business intelligence, from governments to private organizations, from communities to individuals. Big data analytics that discover insights from evidences has a high demand for computing efficiency, knowledge discovery, problem solving, and event prediction. We dedicate a special section of Issue 4/2017 to Big Data. Prospective authors are asked to make the submissions for this section no later than the 31st of May 2017, placing "BigData - " before the paper title in OpenConf.

Read More »


    
 

  4/2018 - 2

Supporting Location Transparent Services in a Mobile Edge Computing Environment

GILLY, K. See more information about GILLY, K. on SCOPUS See more information about GILLY, K. on IEEExplore See more information about GILLY, K. on Web of Science, FILIPOSKA, S. See more information about  FILIPOSKA, S. on SCOPUS See more information about  FILIPOSKA, S. on SCOPUS See more information about FILIPOSKA, S. on Web of Science, MISHEV, A. See more information about MISHEV, A. on SCOPUS See more information about MISHEV, A. on SCOPUS See more information about MISHEV, A. on Web of Science
 
Click to see author's profile in See more information about the author on SCOPUS SCOPUS, See more information about the author on IEEE Xplore IEEE Xplore, See more information about the author on Web of Science Web of Science

Download PDF pdficon (1,495 KB) | Citation | Downloads: 92 | Views: 108

Author keywords
context-aware services, handover, mobile nodes, resource management, wireless networks

References keywords
computing(15), cloud(10), communications(9), internet(7), edge(6), migration(5), virtual(4), survey(4), resource(4), mobile(4)
Blue keywords are present in both the references section and the paper title.

About this article
Date of Publication: 2018-11-30
Volume 18, Issue 4, Year 2018, On page(s): 11 - 22
ISSN: 1582-7445, e-ISSN: 1844-7600
Digital Object Identifier: 10.4316/AECE.2018.04002
Web of Science Accession Number: 000451843400002
SCOPUS ID: 85058806251

Abstract
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Emerging models such as mobile edge computing provide the necessary characteristics for the deployment of Internet of Things applications by supplying the connected devices with local computing facilities essential for latency sensitive applications. One of the major issues of the underlying edge computing architecture is to cope with the device mobility that imposes dynamically changing network requirements. In this paper, we propose a resource management approach that aims to improve the location transparency and provide high quality of experience for end users by optimising latencies perceived when nodes are accessing services hosted on the edge of the network. By managing the virtualised computing resources based on the node location area information, the main objective of the approach is to minimise the network latency perceived by mobile nodes for both the initial allocation and the dynamic resource migration during the service lifetime while the requester node is changing location areas. The system is trying to achieve the most accurate 'follow me' service where the assigned resources closely follow the current mobile node location. The presented results show the effectiveness of the proposed solution in comparison to traditional resource management techniques on the macro and micro scale.


References | Cited By  «-- Click to see who has cited this paper

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[2] R. Mahmud, R. Kotagiri, R. Buyya, "Fog Computing: A Taxonomy, Survey and Future Directions," In: Di Martino B., Li KC., Yang L., Esposito A. (eds) Internet of Everything. Internet of Things (Technology, Communications and Computing). Springer, Singapore, 2018.
[CrossRef]


[3] L. Gao, T. H. Luan, B. Liu, W. Zhou, S. Yu, "Fog computing and its applications in 5G," 5G Mobile Communications, Springer, Oct. 2017, pp. 571-593.
[CrossRef] [SCOPUS Times Cited 9]


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[CrossRef] [SCOPUS Times Cited 1]


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[CrossRef] [Web of Science Times Cited 26] [SCOPUS Times Cited 45]


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[8] S. Clayman, E. Maini, A. Galis, A. Manzalini, N. Mazzocca, "The dynamic placement of virtual network functions," IEEE Network Operations and Management Symposium (NOMS), Krakow, Poland, May 2014.
[CrossRef] [SCOPUS Times Cited 102]


[9] L. F. Bittencourt, M. M. Lopes, I. Petri, O. F. Rana, "Towards virtual machine migration in fog computing," 10th IEEE International Conference on P2P, Parallel, Grid, Cloud and Internet Computing, Krakow, Poland, Nov. 2015.
[CrossRef] [Web of Science Times Cited 17] [SCOPUS Times Cited 29]


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[CrossRef] [SCOPUS Times Cited 23]


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[CrossRef] [SCOPUS Times Cited 23]


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[CrossRef] [Web of Science Times Cited 3] [SCOPUS Times Cited 4]


[13] H. Gupta, A. Vahid Dastjerdi, S. K. Ghosh, R. Buyya, "ifogsim: A toolkit for modeling and simulation of resource management techniques in the internet of things, edge and fog computing environments," Software: Practice and Experience vol. 47 no. 9, 2017, pp. 1275-1296.
[CrossRef] [Web of Science Times Cited 25] [SCOPUS Times Cited 46]


[14] M. Aazam, E.-N. Huh, "Dynamic resource provisioning through fog micro datacenter", IEEE International Conference on Pervasive Computing and Communication Workshops, USA, March 2015.
[CrossRef] [SCOPUS Times Cited 54]


[15] C. T. Do, N. H. Tran, C. Pham, M. G. R. Alam, J. H. Son, C. S. Hong, "A proximal algorithm for joint resource allocation and minimizing carbon footprint in geo-distributed fog computing," 2015 IEEE International Conference on Information Networking (ICOIN), Jan. 2015, pp. 324-329.
[CrossRef] [SCOPUS Times Cited 30]


[16] S. Wang, J. Xu, N. Zhang, Y. Liu, "A Survey on service migration in mobile edge computing," IEEE Access, vol. 6, 2018, pp. 23511-23528.
[CrossRef] [Web of Science Times Cited 7] [SCOPUS Times Cited 7]


[17] K. Velasquez, D. P. Abreu, M. R. M. Assis, C. Senna, D. F. Aranha, L. F. Bittencourt, N. Laranjeiro, M. Curado, M. Vieira, E. Monteiro, E. Madeira, "Fog orchestration for the Internet of Everything: state-of-the-art and research challenges," Journal of Internet Services and Applications 9:14, 2018.
[CrossRef] [Web of Science Times Cited 1] [SCOPUS Times Cited 2]


[18] C. Pahl, B. Lee, "Containers and clusters for edge cloud architectures-a technology review," 3rd IEEE International Conference on Future Internet of Things and Cloud (FiCloud), Rome, Italy, Aug. 2015.
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[19] M. Abo-Zahhad, N. Sabor, S. Sasaki, S. M. Ahmed, "A centralized immune-voronoi deployment algorithm for coverage maximization and energy conservation in mobile wireless sensor networks," Information Fusion vol. 30 issue C, July 2016, pp. 36-51.
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[20] E. G. Coffman Jr, J. Csirik, G. Galambos, S. Martello, D. Vigo, "Bin packing approximation algorithms: survey and classification," Handbook of Combinatorial Optimization, Springer NY, 2013, pp. 455-531.
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[22] R. N. Calheiros, R. Ranjan, A. Beloglazov, C. A. F. De Rose, R. Buyya, "Cloudsim: a toolkit for modeling and simulation of cloud computing environments and evaluation of resource provisioning algorithms," Software: Practice and experience 41 (1) (2011) 23-50.
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[CrossRef] [Web of Science Times Cited 506] [SCOPUS Times Cited 692]




References Weight

Web of Science® Citations for all references: 2,107 TCR
SCOPUS® Citations for all references: 3,609 TCR

Web of Science® Average Citations per reference: 84 ACR
SCOPUS® Average Citations per reference: 144 ACR

TCR = Total Citations for References / ACR = Average Citations per Reference

We introduced in 2010 - for the first time in scientific publishing, the term "References Weight", as a quantitative indication of the quality ... Read more

Citations for references updated on 2019-01-17 07:21 in 162 seconds.




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Disclaimer: All queries to the respective databases were made by using the DOI record of every reference (where available). Due to technical problems beyond our control, the information is not always accurate. Please use the CrossRef link to visit the respective publisher site.

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


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