Authors: Lorena Poenaru-Olaru, Wouter van 't Hof, Adrian Stańdo, Arkadiusz P. Trawiński, Eileen Kapel, Jan S. Rellermeyer, Luís Cruz, Arie van Deursen
Published in: IEEE 36th International Symposium on Software Reliability Engineering (ISSRE) (pp. 394–405).
Abstract: Capacity management is critical for software organizations to allocate resources effectively and meet operational demands. An important step in capacity management is predicting future resource needs often relies on data-driven analytics and machine learning (ML) forecasting models, which require frequent retraining to stay relevant as data evolves. Continuously retraining the forecasting models can be expensive and difficult to scale, posing a challenge for engineering teams tasked with balancing accuracy and efficiency. Retraining only when the data changes appears to be a more computationally efficient alternative, but its impact on accuracy requires further investigation. In this work, we investigate the effects of retraining capacity forecasting models for time series based on detected changes in the data compared to periodic retraining. Our results show that drift-based retraining achieves comparable forecasting accuracy to periodic retraining in most cases, making it a cost-effective strategy. However, in cases where data is changing rapidly, periodic retraining is still preferred to maximize the forecasting accuracy. These findings offer actionable insights for software teams to enhance forecasting systems, reducing retraining overhead while maintaining robust performance.
Bibtex (copy):@INPROCEEDINGS{poenaru2025prepared,
author={Lorena Poenaru-Olaru and Wouter van 't Hof and Adrian Sta\'{n}do and Arkadiusz P. Trawi\'{n}ski and Eileen Kapel and Jan S. Rellermeyer and Lu\'{i}s Cruz and Arie van Deursen},
booktitle={2025 IEEE 36th International Symposium on Software Reliability Engineering (ISSRE)},
title={Prepared for the Unknown: Adapting AIOps Capacity Forecasting Models to Data Changes},
year={2025},
pages={394--405},
doi={10.1109/ISSRE66568.2025.00047}}Read me: Arxiv. DOI: 10.1109/ISSRE66568.2025.00047.