TY - GEN

T1 - Prepared for the Unknown

T2 - Adapting AIOps Capacity Forecasting Models to Data Changes.

AU - Poenaru-Olaru, Lorena

AU - Hof, Wouter van 't

AU - Stando, Adrian

AU - Trawinski, Arkadiusz P.

AU - Kapel, Eileen

AU - Rellermeyer, Jan S.

AU - Miranda da Cruz, Luis

AU - van Deursen, Arie

N1 - DBLP License: DBLP's bibliographic metadata records provided through http://dblp.org/ are distributed under a Creative Commons CC0 1.0 Universal Public Domain Dedication. Although the bibliographic metadata records are provided consistent with CC0 1.0 Dedication, the content described by the metadata records is not. Content may be subject to copyright, rights of privacy, rights of publicity and other restrictions.

PY - 2025/10/21

Y1 - 2025/10/21

N2 - 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 costeffective 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.

AB - 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 costeffective 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.

KW - concept drift detection

KW - retraining based on drift detection

KW - time series forecasting

UR - http://www.scopus.com/inward/record.url?scp=105026754044&partnerID=8YFLogxK

U2 - 10.1109/ISSRE66568.2025.00047

DO - 10.1109/ISSRE66568.2025.00047

M3 - Conference contribution

SN - 979-8-3503-9303-3

T3 - Proceedings - International Symposium on Software Reliability Engineering, ISSRE

SP - 394

EP - 405

BT - 2025 IEEE 36th International Symposium on Software Reliability Engineering (ISSRE)

ER -