Vortrag: Machine Learning Based Resource Utilization Prediction in the Computi...
Stammdaten
Titel: | Machine Learning Based Resource Utilization Prediction in the Computing Continuum |
Beschreibung: | This paper presents UtilML, a novel approach for tackling resource utilization prediction challenges in the computing continuum. UtilML leverages Long-Short-Term Memory (LSTM) neural networks, a machine learning technique, to forecast resource utilization accurately. The effectiveness of UtilML is demonstrated through its evaluation of data extracted from a real GPU cluster in a computing continuum infrastructure comprising more than 1800 computing devices. To assess the performance of UtilML, we compared it with two related approaches that utilize a Baseline-LSTM model. Furthermore, we analyzed the LSTM results against User-Predicted values provided by GPU cluster owners for task deployment with estimated allocation values. The results indicate that UtilML outperformed user predictions by 2% to 27% for CPU utilization prediction. For memory prediction, UtilML variants excelled, showing improvements of 17% to 20% compared to user predictions. |
Schlagworte: | Utilization Prediction, Machine Learning, Computing Continuum, Cloud |
Typ: | Angemeldeter Vortrag |
Homepage: | https://camad2023.ieee-camad.org/ |
Veranstaltung: | IEEE International Workshop on Computer Aided Modeling and Design of Communication Links and Networks (CAMAD 2023) (Edinburgh) |
Datum: | 07.11.2023 |
Vortragsstatus: | stattgefunden (Präsenz) |
Beteiligte
Christian Bauer (intern) |
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Narges Mehran (intern) |
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Radu Aurel Prodan (intern) |
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Dragi Kimovski (intern) |
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Zuordnung
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Fakultät für Technische Wissenschaften
Institut für Informationstechnologie
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AT - 9020 Klagenfurt am Wörthersee |
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