On Learning Parametric Dependencies from Monitoring Data
| dc.contributor.author | Grohmann, Johannes | |
| dc.contributor.author | Eismann, Simon | |
| dc.contributor.author | Kounev, Samuel | |
| dc.contributor.editor | Kelter, Udo | |
| dc.date.accessioned | 2023-02-27T13:59:28Z | |
| dc.date.available | 2023-02-27T13:59:28Z | |
| dc.date.issued | 2019 | |
| dc.description.abstract | A common approach to predict system performance are so-called architectural performance models. In these models, parametric dependencies describe the relation between the input parameters of a component and its performance properties and therefore significantly increase the model expressiveness. However, manually modeling parametric dependencies is often infeasible in practice. Existing automated extraction approaches require either application source code or dedicated performance tests, which are not always available. We therefore introduced one approach for identification and one for characterization of parametric dependencies, solely based on run-time monitoring data. In this paper, we propose our idea on combining both techniques in order to create a holistic approach for the identification and characterization of parametric dependencies. Furthermore, we discuss challenges we are currently facing and potential ideas on how to overcome them. | en |
| dc.identifier.pissn | 0720-8928 | |
| dc.identifier.uri | https://dl.gi.de/handle/20.500.12116/40492 | |
| dc.language.iso | en | |
| dc.publisher | Gesellschaft für Informatik e.V. | |
| dc.relation.ispartof | Softwaretechnik-Trends Band 39, Heft 4 | |
| dc.relation.ispartofseries | Softwaretechnik-Trends | |
| dc.subject | architectural performance model | |
| dc.subject | run-time monitoring | |
| dc.title | On Learning Parametric Dependencies from Monitoring Data | en |
| dc.type | Text/Conference Paper | |
| gi.citation.endPage | 16 | |
| gi.citation.publisherPlace | Bonn | |
| gi.citation.startPage | 14 | |
| gi.conference.date | 5.-6. November 2019 | |
| gi.conference.location | Würzburg | |
| gi.conference.sessiontitle | 10th Symposium on Software Performance (SSP) |
Dateien
Originalbündel
1 - 1 von 1
Vorschaubild nicht verfügbar
- Name:
- SSP2019_Grohmann.pdf
- Größe:
- 188.15 KB
- Format:
- Adobe Portable Document Format