IEEE International Workshop on Computer Aided Modeling and Design of Communication Links and Networks
6–8 November 2023 | Edinburgh, Scotland
[PDF][Slides]
Christian Bauer (Alpen-Adria-Universität Klagenfurt), Narges Mehran (Alpen-Adria-Universität Klagenfurt), Radu Prodan (Alpen-Adria-Universität Klagenfurt) and Dragi Kimovski (Alpen-Adria-Universität Klagenfurt)
Abstract: 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.
Keywords: Utilization Prediction, Machine Learning, Computing Continuum, Cloud.
