A Model-Based Scalability Optimization Methodology for Cloud Applications

Jia Chun Lin; Jacopo Mauro; Thomas Brox Røst; Ingrid Chieh Yu

doi:10.1109/SC2.2017.32

A Model-Based Scalability Optimization Methodology for Cloud Applications

Jia Chun Lin, Jacopo Mauro, Thomas Brox Røst, Ingrid Chieh Yu

Research output: Chapter in Book/Report/Conference proceeding › Article in proceedings › Research › peer-review

Abstract

Complex applications composed of many interconnected but functionally independent services or components are widely adopted and deployed on the cloud to exploit its elasticity. This allows the application to react to load changes by varying the amount of computational resources used. Deciding the proper scaling settings for a complex architecture is, however, a daunting task: many possible settings exists with big repercussions in terms of performance and cost. In this paper, we present a methodology that, by relying on modeling and automatic parameter configurators, allows to understand the best way to configure the scalability of the application to be deployed on the cloud. We exemplify the approach by using an existing service-oriented framework to dispatch car software updates.

Original language	English
Title of host publication	Proceedings - 2017 IEEE 7th International Symposium on Cloud and Service Computing, SC2 2017
Number of pages	8
Publisher	IEEE
Publication date	13. Mar 2018
Pages	163-170
ISBN (Electronic)	9780769563282
DOIs	https://doi.org/10.1109/SC2.2017.32
Publication status	Published - 13. Mar 2018
Externally published	Yes
Event	7th IEEE International Symposium on Cloud and Service Computing, SC2 2017 - Kanazawa, Japan Duration: 22. Nov 2017 → 25. Nov 2017

Conference

Conference	7th IEEE International Symposium on Cloud and Service Computing, SC2 2017
Country/Territory	Japan
City	Kanazawa
Period	22/11/2017 → 25/11/2017

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10.1109/SC2.2017.32

Cite this

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title = "A Model-Based Scalability Optimization Methodology for Cloud Applications",

abstract = "Complex applications composed of many interconnected but functionally independent services or components are widely adopted and deployed on the cloud to exploit its elasticity. This allows the application to react to load changes by varying the amount of computational resources used. Deciding the proper scaling settings for a complex architecture is, however, a daunting task: many possible settings exists with big repercussions in terms of performance and cost. In this paper, we present a methodology that, by relying on modeling and automatic parameter configurators, allows to understand the best way to configure the scalability of the application to be deployed on the cloud. We exemplify the approach by using an existing service-oriented framework to dispatch car software updates.",

author = "Lin, {Jia Chun} and Jacopo Mauro and R{\o}st, {Thomas Brox} and Yu, {Ingrid Chieh}",

year = "2018",

month = mar,

day = "13",

doi = "10.1109/SC2.2017.32",

language = "English",

pages = "163--170",

booktitle = "Proceedings - 2017 IEEE 7th International Symposium on Cloud and Service Computing, SC2 2017",

publisher = "IEEE",

address = "United States",

note = "7th IEEE International Symposium on Cloud and Service Computing, SC2 2017 ; Conference date: 22-11-2017 Through 25-11-2017",

}

Lin, JC, Mauro, J, Røst, TB & Yu, IC 2018, A Model-Based Scalability Optimization Methodology for Cloud Applications. in Proceedings - 2017 IEEE 7th International Symposium on Cloud and Service Computing, SC2 2017. IEEE, pp. 163-170, 7th IEEE International Symposium on Cloud and Service Computing, SC2 2017, Kanazawa, Japan, 22/11/2017. https://doi.org/10.1109/SC2.2017.32

TY - GEN

T1 - A Model-Based Scalability Optimization Methodology for Cloud Applications

AU - Lin, Jia Chun

AU - Mauro, Jacopo

AU - Røst, Thomas Brox

AU - Yu, Ingrid Chieh

PY - 2018/3/13

Y1 - 2018/3/13

N2 - Complex applications composed of many interconnected but functionally independent services or components are widely adopted and deployed on the cloud to exploit its elasticity. This allows the application to react to load changes by varying the amount of computational resources used. Deciding the proper scaling settings for a complex architecture is, however, a daunting task: many possible settings exists with big repercussions in terms of performance and cost. In this paper, we present a methodology that, by relying on modeling and automatic parameter configurators, allows to understand the best way to configure the scalability of the application to be deployed on the cloud. We exemplify the approach by using an existing service-oriented framework to dispatch car software updates.

AB - Complex applications composed of many interconnected but functionally independent services or components are widely adopted and deployed on the cloud to exploit its elasticity. This allows the application to react to load changes by varying the amount of computational resources used. Deciding the proper scaling settings for a complex architecture is, however, a daunting task: many possible settings exists with big repercussions in terms of performance and cost. In this paper, we present a methodology that, by relying on modeling and automatic parameter configurators, allows to understand the best way to configure the scalability of the application to be deployed on the cloud. We exemplify the approach by using an existing service-oriented framework to dispatch car software updates.

U2 - 10.1109/SC2.2017.32

DO - 10.1109/SC2.2017.32

M3 - Article in proceedings

AN - SCOPUS:85047813679

SP - 163

EP - 170

BT - Proceedings - 2017 IEEE 7th International Symposium on Cloud and Service Computing, SC2 2017

PB - IEEE

T2 - 7th IEEE International Symposium on Cloud and Service Computing, SC2 2017

Y2 - 22 November 2017 through 25 November 2017

ER -

A Model-Based Scalability Optimization Methodology for Cloud Applications

Abstract

Conference

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