Machine Learning to Predict Risk Management Applications Performance
Year: 2024
Type: conference-paper
Abstract: Machine Learning is increasingly crucial for predicting application performance, offering a black-box approach that does not require a deep understanding of the application internal workings. This method enables accurate predictions without delving into complex system models. Our study utilized ML to forecast the execution time of an industrial application dealing with risk measures as part of the Solvency II regulations for insurance companies. By conducting a comparative analysis of multiple models, XGBoost was identified as the most effective, achieving a Mean Absolute Percentage Error of 18%. The results demonstrated robust accuracy for intermediate durations, though limitations were observed for shorter and significantly longer times due to data scarcity. Overall, this study highlights the significant potential of ML in improving prediction accuracy for complex industrial applications, offering valuable insights for resource allocation and performance management.
Authors: Laura De Giorgi, Danilo Ardagna
Institution: Politecnico di Milano
Language: English
Subfield: Computer Networks and Communications
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W4404916275