Using Multi-System Monitoring Time Series to Predict Performance Events
| dc.contributor.author | Schörgenhumer, Andreas | |
| dc.contributor.author | Kahlhofer, Mario | |
| dc.contributor.author | Chalupar, Peter | |
| dc.contributor.author | Mössenböck, Hanspeter | |
| dc.contributor.author | Grünbacher, Paul | |
| dc.contributor.editor | Kelter, Udo | |
| dc.date.accessioned | 2023-02-27T13:46:43Z | |
| dc.date.available | 2023-02-27T13:46:43Z | |
| dc.date.issued | 2019 | |
| dc.description.abstract | The prediction of failures and other mission-critical events plays an important role in operating today’s software systems and has drawn the attention of many researchers. Event prediction is particularly challenging if multiple systems are involved. In this paper, we thus present an event prediction model which utilizes time series monitoring data from multiple software systems to predict performance events. Our approach incorporates a comprehensive, multi-system data preprocessing framework for creating various feature vector sets, which we then use to train a random forest classifier to evaluate our multi-system event prediction. Our preliminary evaluation based on data from monitoring 250 systems over a period of 20 days shows promising results. | en |
| dc.identifier.pissn | 0720-8928 | |
| dc.identifier.uri | https://dl.gi.de/handle/20.500.12116/40467 | |
| dc.language.iso | en | |
| dc.publisher | Gesellschaft für Informatik e.V. | |
| dc.relation.ispartof | Softwaretechnik-Trends Band 39, Heft 3 | |
| dc.relation.ispartofseries | Softwaretechnik-Trends | |
| dc.subject | prediction | |
| dc.subject | failures | |
| dc.subject | prediction model | |
| dc.subject | time series | |
| dc.subject | performance | |
| dc.title | Using Multi-System Monitoring Time Series to Predict Performance Events | en |
| dc.type | Text/Conference Paper | |
| gi.citation.endPage | 57 | |
| gi.citation.publisherPlace | Bonn | |
| gi.citation.startPage | 55 | |
| gi.conference.date | 8.-9. November 2018 | |
| gi.conference.location | Hildesheim | |
| gi.conference.sessiontitle | 9th Symposium on Software Performance (SSP 2018) |
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