Abstract
The extension of the Cloud to the Edge of the network through Fog Computing can have a significant impact on the reliability and latencies of deployed applications. Recent papers have suggested a shift from VM and Container based deployments to a shared environment among applications to better utilize resources. Unfortunately, the existing deployment and optimization methods pay little attention to developing and identifying complete models to such systems which may cause large inaccuracies between simulated and physical run-time parameters. Existing models do not account for application interdependence or the locality of application resources which causes extra communication and processing delays. This paper addresses these issues by carrying out experiments in both cloud and edge systems with various scales and applications. It analyses the outcomes to derive a new reference model with data driven parameter formulations and representations to help understand the effect of migration on these systems. As a result, we can have a more complete characterization of the fog environment. This, together with tailored optimization methods than can handle the heterogeneity and scale of the fog can improve the overall system run-time parameters and improve constraint satisfaction. An Industry 4.0 based case study with different scenarios was used to analyze and validate the effectiveness of the proposed model. Tests were deployed on physical and virtual environments with different scales. The advantages of the model based optimization methods were validated in real physical environments. Based on these tests, we have found that our model is 90% accurate on load and delay predictions for application deployments in both cloud and edge.
| Original language | English |
|---|---|
| Pages (from-to) | 48-60 |
| Number of pages | 13 |
| Journal | Future Generation Computer Systems |
| Volume | 91 |
| Early online date | 1 Sep 2018 |
| DOIs | |
| Publication status | Published - Feb 2019 |
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Bibliographical note
NOTICE: this is the author’s version of a work that was accepted for publication in Future Generation Computer Systems. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. A definitive version was subsequently published in Future Generation Computer Systems, vol. 91, 2019 DOI: 10.1016/j.future.2018.08.043© 2017, Elsevier. Licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International http://creativecommons.org/licenses/by-nc-nd/4.0/
Keywords
- Application model
- Cloud computing
- Fog computing
- Migration
ASJC Scopus subject areas
- Software
- Hardware and Architecture
- Computer Networks and Communications
Cite this
Modeling industry 4.0 based fog computing environments for application analysis and deployment. / Verba, Nandor; Chao, Kuo-Ming; Lewandowski, Jacek; Shah, Nazaraf; James, Anne; Tian, Feng.
In: Future Generation Computer Systems, Vol. 91, 02.2019, p. 48-60.Research output: Contribution to journal › Article
}
TY - JOUR
T1 - Modeling industry 4.0 based fog computing environments for application analysis and deployment
AU - Verba, Nandor
AU - Chao, Kuo-Ming
AU - Lewandowski, Jacek
AU - Shah, Nazaraf
AU - James, Anne
AU - Tian, Feng
N1 - NOTICE: this is the author’s version of a work that was accepted for publication in Future Generation Computer Systems. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. A definitive version was subsequently published in Future Generation Computer Systems, vol. 91, 2019 DOI: 10.1016/j.future.2018.08.043 © 2017, Elsevier. Licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International http://creativecommons.org/licenses/by-nc-nd/4.0/
PY - 2019/2
Y1 - 2019/2
N2 - The extension of the Cloud to the Edge of the network through Fog Computing can have a significant impact on the reliability and latencies of deployed applications. Recent papers have suggested a shift from VM and Container based deployments to a shared environment among applications to better utilize resources. Unfortunately, the existing deployment and optimization methods pay little attention to developing and identifying complete models to such systems which may cause large inaccuracies between simulated and physical run-time parameters. Existing models do not account for application interdependence or the locality of application resources which causes extra communication and processing delays. This paper addresses these issues by carrying out experiments in both cloud and edge systems with various scales and applications. It analyses the outcomes to derive a new reference model with data driven parameter formulations and representations to help understand the effect of migration on these systems. As a result, we can have a more complete characterization of the fog environment. This, together with tailored optimization methods than can handle the heterogeneity and scale of the fog can improve the overall system run-time parameters and improve constraint satisfaction. An Industry 4.0 based case study with different scenarios was used to analyze and validate the effectiveness of the proposed model. Tests were deployed on physical and virtual environments with different scales. The advantages of the model based optimization methods were validated in real physical environments. Based on these tests, we have found that our model is 90% accurate on load and delay predictions for application deployments in both cloud and edge.
AB - The extension of the Cloud to the Edge of the network through Fog Computing can have a significant impact on the reliability and latencies of deployed applications. Recent papers have suggested a shift from VM and Container based deployments to a shared environment among applications to better utilize resources. Unfortunately, the existing deployment and optimization methods pay little attention to developing and identifying complete models to such systems which may cause large inaccuracies between simulated and physical run-time parameters. Existing models do not account for application interdependence or the locality of application resources which causes extra communication and processing delays. This paper addresses these issues by carrying out experiments in both cloud and edge systems with various scales and applications. It analyses the outcomes to derive a new reference model with data driven parameter formulations and representations to help understand the effect of migration on these systems. As a result, we can have a more complete characterization of the fog environment. This, together with tailored optimization methods than can handle the heterogeneity and scale of the fog can improve the overall system run-time parameters and improve constraint satisfaction. An Industry 4.0 based case study with different scenarios was used to analyze and validate the effectiveness of the proposed model. Tests were deployed on physical and virtual environments with different scales. The advantages of the model based optimization methods were validated in real physical environments. Based on these tests, we have found that our model is 90% accurate on load and delay predictions for application deployments in both cloud and edge.
KW - Application model
KW - Cloud computing
KW - Fog computing
KW - Migration
UR - http://www.scopus.com/inward/record.url?scp=85052964359&partnerID=8YFLogxK
U2 - 10.1016/j.future.2018.08.043
DO - 10.1016/j.future.2018.08.043
M3 - Article
VL - 91
SP - 48
EP - 60
JO - Future Generation Computer Systems
JF - Future Generation Computer Systems
SN - 0167-739X
ER -