Searching for optimal configurations within large-scale models: a cloud computing domain

Lina Ochoa; Oscar González-Rojas; Mauricio Verano; Harold Castro

doi:10.1007/978-3-319-47717-6_6

Searching for optimal configurations within large-scale models: a cloud computing domain

Lina Ochoa, Oscar González-Rojas, Mauricio Verano, Harold Castro

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › Academic › peer-review

5 Citations (Scopus)

Abstract

Feature modeling is a widely accepted variability modeling technique for supporting decision-making scenarios, by representing decisions as features. However, there are scenarios where domain concepts have multiple implementation alternatives that have to be analyzed from large-scale data sources. Therefore, a manual selection of an optimal solution from within the alternatives space or even the complete representation of the domain is an unsuitable task. To solve this issue, we created a feature modeling metamodel and two specific processes to represent domain and implementation alternative models, and to search for optimal solutions whilst considering a set of optimization objectives. We applied this approach to a cloud computing case study and obtained an optimal provider configuration for deploying a JEE application. © Springer International Publishing AG 2016.

Original language	English
Title of host publication	Advances in Conceptual Modeling
Subtitle of host publication	ER 2016 Workshops, AHA, MoBiD, MORE-BI, MReBA, QMMQ, SCME, and WM2SP, Gifu, Japan, November 14–17, 2016, Proceedings
Editors	S. Link, J.C. Trujillo
Place of Publication	Dordrecht
Publisher	Springer
Pages	65-75
Number of pages	11
ISBN (Electronic)	978-3-319-47717-6
ISBN (Print)	978-3-319-47716-9
DOIs	https://doi.org/10.1007/978-3-319-47717-6_6
Publication status	Published - 2016
Event	35th International Conference on Conceptual Modeling, ER 2016 - Gifu, Japan Duration: 14 Nov 2016 → 17 Nov 2016 Conference number: 35 http://er2016.cs.titech.ac.jp/

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	9975 LNCS

Conference

Conference	35th International Conference on Conceptual Modeling, ER 2016
Abbreviated title	ER 2016
Country/Territory	Japan
City	Gifu
Period	14/11/16 → 17/11/16
Internet address	http://er2016.cs.titech.ac.jp/

Keywords

Big data
Cloud
Conceptual modeling
Decision-making

Access to Document

10.1007/978-3-319-47717-6_6

http://www.mendeley.com/research/searching-optimal-configurations-within-largescale-models-cloud-computing-domain

Cite this

Ochoa, L., González-Rojas, O., Verano, M., & Castro, H. (2016). Searching for optimal configurations within large-scale models: a cloud computing domain. In S. Link, & J. C. Trujillo (Eds.), Advances in Conceptual Modeling: ER 2016 Workshops, AHA, MoBiD, MORE-BI, MReBA, QMMQ, SCME, and WM2SP, Gifu, Japan, November 14–17, 2016, Proceedings (pp. 65-75). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 9975 LNCS). Springer. https://doi.org/10.1007/978-3-319-47717-6_6

Ochoa, Lina ; González-Rojas, Oscar ; Verano, Mauricio et al. / Searching for optimal configurations within large-scale models: a cloud computing domain. Advances in Conceptual Modeling: ER 2016 Workshops, AHA, MoBiD, MORE-BI, MReBA, QMMQ, SCME, and WM2SP, Gifu, Japan, November 14–17, 2016, Proceedings. editor / S. Link ; J.C. Trujillo. Dordrecht : Springer, 2016. pp. 65-75 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

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abstract = "Feature modeling is a widely accepted variability modeling technique for supporting decision-making scenarios, by representing decisions as features. However, there are scenarios where domain concepts have multiple implementation alternatives that have to be analyzed from large-scale data sources. Therefore, a manual selection of an optimal solution from within the alternatives space or even the complete representation of the domain is an unsuitable task. To solve this issue, we created a feature modeling metamodel and two specific processes to represent domain and implementation alternative models, and to search for optimal solutions whilst considering a set of optimization objectives. We applied this approach to a cloud computing case study and obtained an optimal provider configuration for deploying a JEE application. {\textcopyright} Springer International Publishing AG 2016.",

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Ochoa, L, González-Rojas, O, Verano, M & Castro, H 2016, Searching for optimal configurations within large-scale models: a cloud computing domain. in S Link & JC Trujillo (eds), Advances in Conceptual Modeling: ER 2016 Workshops, AHA, MoBiD, MORE-BI, MReBA, QMMQ, SCME, and WM2SP, Gifu, Japan, November 14–17, 2016, Proceedings. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 9975 LNCS, Springer, Dordrecht, pp. 65-75, 35th International Conference on Conceptual Modeling, ER 2016, Gifu, Japan, 14/11/16. https://doi.org/10.1007/978-3-319-47717-6_6

Searching for optimal configurations within large-scale models: a cloud computing domain. / Ochoa, Lina; González-Rojas, Oscar; Verano, Mauricio et al.
Advances in Conceptual Modeling: ER 2016 Workshops, AHA, MoBiD, MORE-BI, MReBA, QMMQ, SCME, and WM2SP, Gifu, Japan, November 14–17, 2016, Proceedings. ed. / S. Link; J.C. Trujillo. Dordrecht: Springer, 2016. p. 65-75 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 9975 LNCS).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › Academic › peer-review

TY - GEN

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AU - Verano, Mauricio

AU - Castro, Harold

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N2 - Feature modeling is a widely accepted variability modeling technique for supporting decision-making scenarios, by representing decisions as features. However, there are scenarios where domain concepts have multiple implementation alternatives that have to be analyzed from large-scale data sources. Therefore, a manual selection of an optimal solution from within the alternatives space or even the complete representation of the domain is an unsuitable task. To solve this issue, we created a feature modeling metamodel and two specific processes to represent domain and implementation alternative models, and to search for optimal solutions whilst considering a set of optimization objectives. We applied this approach to a cloud computing case study and obtained an optimal provider configuration for deploying a JEE application. © Springer International Publishing AG 2016.

AB - Feature modeling is a widely accepted variability modeling technique for supporting decision-making scenarios, by representing decisions as features. However, there are scenarios where domain concepts have multiple implementation alternatives that have to be analyzed from large-scale data sources. Therefore, a manual selection of an optimal solution from within the alternatives space or even the complete representation of the domain is an unsuitable task. To solve this issue, we created a feature modeling metamodel and two specific processes to represent domain and implementation alternative models, and to search for optimal solutions whilst considering a set of optimization objectives. We applied this approach to a cloud computing case study and obtained an optimal provider configuration for deploying a JEE application. © Springer International Publishing AG 2016.

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Ochoa L, González-Rojas O, Verano M, Castro H. Searching for optimal configurations within large-scale models: a cloud computing domain. In Link S, Trujillo JC, editors, Advances in Conceptual Modeling: ER 2016 Workshops, AHA, MoBiD, MORE-BI, MReBA, QMMQ, SCME, and WM2SP, Gifu, Japan, November 14–17, 2016, Proceedings. Dordrecht: Springer. 2016. p. 65-75. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). doi: 10.1007/978-3-319-47717-6_6

Searching for optimal configurations within large-scale models: a cloud computing domain

Abstract

Publication series

Conference

Keywords

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