Assessing SLA compliance from Palladio component models
Service providers face the challenge of meeting service-level agreements (SLAs) under uncertainty on the application actual performance. The performance heavily depends on the characteristics of the hardware on which the application is deployed, on the application architecture, as well as on the use...
- Autores:
- Tipo de recurso:
- Fecha de publicación:
- 2014
- Institución:
- Universidad del Rosario
- Repositorio:
- Repositorio EdocUR - U. Rosario
- Idioma:
- eng
- OAI Identifier:
- oai:repository.urosario.edu.co:10336/28496
- Acceso en línea:
- https://doi.org/10.1109/SYNASC.2013.60
https://repository.urosario.edu.co/handle/10336/28496
- Palabra clave:
- Computational modeling
Analytical models
Phase change materials
Program processors
Servers
Delays
Throughput
- Rights
- License
- Restringido (Acceso a grupos específicos)
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80035202600693f31c7-5e59-4d88-b96a-2175836ec1b02020-08-28T15:49:14Z2020-08-28T15:49:14Z2014-05-26Service providers face the challenge of meeting service-level agreements (SLAs) under uncertainty on the application actual performance. The performance heavily depends on the characteristics of the hardware on which the application is deployed, on the application architecture, as well as on the user workload. Although many models have been proposed for the performance prediction of software applications, most of them focus on average measures, e.g., mean response times. However, SLAs are often set in terms of percentiles, such that a given portion of requests receive a predefined service level, e.g., 95% of the requests should face a response time of at most 10 ms. To enable the effective prediction of this type of measures, in this paper we use fluid models for the computation of the probability distribution of performance measures relevant for SLAs. Our models are automatically built from a Palladio Component Model (PCM) instance, thus allowing the SLA assessment directly from the PCM specification. This provides an scalable alternative for SLA assessment within the PCM framework, as currently this is supported by means of simulation only.application/pdfhttps://doi.org/10.1109/SYNASC.2013.60ISBN: 978-1-4799-3035-7EISBN: 978-1-4799-3036-4https://repository.urosario.edu.co/handle/10336/28496engIEEE4164092013 15th International Symposium on Symbolic and Numeric Algorithms for Scientific Computing15th International Symposium on Symbolic and Numeric Algorithms for Scientific Computing, ISBN: 978-1-4799-3035-7;EISBN: 978-1-4799-3036-4 (2013); pp. 409-416https://ieeexplore.ieee.org/document/6821177?section=abstractRestringido (Acceso a grupos específicos)http://purl.org/coar/access_right/c_16ec2013 15th International Symposium on Symbolic and Numeric Algorithms for Scientific Computinginstname:Universidad del Rosarioreponame:Repositorio Institucional EdocURComputational modelingAnalytical modelsPhase change materialsProgram processorsServersDelaysThroughputAssessing SLA compliance from Palladio component modelsEvaluación del cumplimiento de SLA a partir de modelos de componentes de PalladiobookPartParte de librohttp://purl.org/coar/version/c_970fb48d4fbd8a85http://purl.org/coar/resource_type/c_3248Pérez, Juan F.Casale, Giuliano10336/28496oai:repository.urosario.edu.co:10336/284962021-09-23 12:33:13.786https://repository.urosario.edu.coRepositorio institucional EdocURedocur@urosario.edu.co |
dc.title.spa.fl_str_mv |
Assessing SLA compliance from Palladio component models |
dc.title.TranslatedTitle.spa.fl_str_mv |
Evaluación del cumplimiento de SLA a partir de modelos de componentes de Palladio |
title |
Assessing SLA compliance from Palladio component models |
spellingShingle |
Assessing SLA compliance from Palladio component models Computational modeling Analytical models Phase change materials Program processors Servers Delays Throughput |
title_short |
Assessing SLA compliance from Palladio component models |
title_full |
Assessing SLA compliance from Palladio component models |
title_fullStr |
Assessing SLA compliance from Palladio component models |
title_full_unstemmed |
Assessing SLA compliance from Palladio component models |
title_sort |
Assessing SLA compliance from Palladio component models |
dc.subject.keyword.spa.fl_str_mv |
Computational modeling Analytical models Phase change materials Program processors Servers Delays Throughput |
topic |
Computational modeling Analytical models Phase change materials Program processors Servers Delays Throughput |
description |
Service providers face the challenge of meeting service-level agreements (SLAs) under uncertainty on the application actual performance. The performance heavily depends on the characteristics of the hardware on which the application is deployed, on the application architecture, as well as on the user workload. Although many models have been proposed for the performance prediction of software applications, most of them focus on average measures, e.g., mean response times. However, SLAs are often set in terms of percentiles, such that a given portion of requests receive a predefined service level, e.g., 95% of the requests should face a response time of at most 10 ms. To enable the effective prediction of this type of measures, in this paper we use fluid models for the computation of the probability distribution of performance measures relevant for SLAs. Our models are automatically built from a Palladio Component Model (PCM) instance, thus allowing the SLA assessment directly from the PCM specification. This provides an scalable alternative for SLA assessment within the PCM framework, as currently this is supported by means of simulation only. |
publishDate |
2014 |
dc.date.created.spa.fl_str_mv |
2014-05-26 |
dc.date.accessioned.none.fl_str_mv |
2020-08-28T15:49:14Z |
dc.date.available.none.fl_str_mv |
2020-08-28T15:49:14Z |
dc.type.eng.fl_str_mv |
bookPart |
dc.type.coarversion.fl_str_mv |
http://purl.org/coar/version/c_970fb48d4fbd8a85 |
dc.type.coar.fl_str_mv |
http://purl.org/coar/resource_type/c_3248 |
dc.type.spa.spa.fl_str_mv |
Parte de libro |
dc.identifier.doi.none.fl_str_mv |
https://doi.org/10.1109/SYNASC.2013.60 |
dc.identifier.issn.none.fl_str_mv |
ISBN: 978-1-4799-3035-7 EISBN: 978-1-4799-3036-4 |
dc.identifier.uri.none.fl_str_mv |
https://repository.urosario.edu.co/handle/10336/28496 |
url |
https://doi.org/10.1109/SYNASC.2013.60 https://repository.urosario.edu.co/handle/10336/28496 |
identifier_str_mv |
ISBN: 978-1-4799-3035-7 EISBN: 978-1-4799-3036-4 |
dc.language.iso.spa.fl_str_mv |
eng |
language |
eng |
dc.relation.citationEndPage.none.fl_str_mv |
416 |
dc.relation.citationStartPage.none.fl_str_mv |
409 |
dc.relation.citationTitle.none.fl_str_mv |
2013 15th International Symposium on Symbolic and Numeric Algorithms for Scientific Computing |
dc.relation.ispartof.spa.fl_str_mv |
15th International Symposium on Symbolic and Numeric Algorithms for Scientific Computing, ISBN: 978-1-4799-3035-7;EISBN: 978-1-4799-3036-4 (2013); pp. 409-416 |
dc.relation.uri.spa.fl_str_mv |
https://ieeexplore.ieee.org/document/6821177?section=abstract |
dc.rights.coar.fl_str_mv |
http://purl.org/coar/access_right/c_16ec |
dc.rights.acceso.spa.fl_str_mv |
Restringido (Acceso a grupos específicos) |
rights_invalid_str_mv |
Restringido (Acceso a grupos específicos) http://purl.org/coar/access_right/c_16ec |
dc.format.mimetype.none.fl_str_mv |
application/pdf |
dc.publisher.spa.fl_str_mv |
IEEE |
dc.source.spa.fl_str_mv |
2013 15th International Symposium on Symbolic and Numeric Algorithms for Scientific Computing |
institution |
Universidad del Rosario |
dc.source.instname.none.fl_str_mv |
instname:Universidad del Rosario |
dc.source.reponame.none.fl_str_mv |
reponame:Repositorio Institucional EdocUR |
repository.name.fl_str_mv |
Repositorio institucional EdocUR |
repository.mail.fl_str_mv |
edocur@urosario.edu.co |
_version_ |
1814167695253307392 |