7 citations · 10 across the 5 of their papers we have counts for
5 papers
Prepared for the Unknown: Adapting AIOps Capacity Forecasting Models to Data Changes
Lorena Poenaru-Olaru, Wouter van 't Hof, Adrian Stando +5
Capacity management is critical for software organizations to allocate resources effectively and meet operational demands. An important step in capacity management is predicting fu…
Sustainable Machine Learning Retraining: Optimizing Energy Efficiency Without Compromising Accuracy
Lorena Poenaru-Olaru, June Sallou, Luis Cruz +2
The reliability of machine learning (ML) software systems is heavily influenced by changes in data over time. For that reason, ML systems require regular maintenance, typically bas…
McUDI: Model-Centric Unsupervised Degradation Indicator for Failure Prediction AIOps Solutions
Lorena Poenaru-Olaru, Luis Cruz, Jan Rellermeyer +1
Due to the continuous change in operational data, AIOps solutions suffer from performance degradation over time. Although periodic retraining is the state-of-the-art technique to p…
Is Your Anomaly Detector Ready for Change? Adapting AIOps Solutions to the Real World
Lorena Poenaru-Olaru, Natalia Karpova, Luis Cruz +2
Anomaly detection techniques are essential in automating the monitoring of IT systems and operations. These techniques imply that machine learning algorithms are trained on operati…
Are Concept Drift Detectors Reliable Alarming Systems? -- A Comparative Study
Lorena Poenaru-Olaru, Luis Cruz, Arie van Deursen +1
As machine learning models increasingly replace traditional business logic in the production system, their lifecycle management is becoming a significant concern. Once deployed int…