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…
COPR -- Efficient, large-scale log storage and retrieval
Julian Reichinger, Thomas Krismayer, Jan Rellermeyer
Modern, large scale monitoring systems have to process and store vast amounts of log data in near real-time. At query time the systems have to find relevant logs based on the conte…
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…