Hannah Stein presented our full research paper at the 38th International Conference on Advanced Information Systems Engineering (CAiSE 2026) in Verona, Italy. With an acceptance rate of approximately 14% for full research papers, CAiSE 2026 offered a highly selective forum for presenting advances in Information Systems Engineering. The paper, titled "Towards Decision Support Systems for Cost-Effective and Energy-Efficient AI Operations in Data Centers," addresses the increasing energy demand of AI workloads and the resulting operational costs and environmental impacts.
Although various technical approaches aim to improve the energy efficiency of data centers, existing solutions often remain fragmented. As a result, organizations lack integrated transparency and actionable guidance for balancing AI performance, energy consumption, costs, and environmental impact. The paper introduces an Information Systems perspective on this challenge and investigates how decision support systems can enable more sustainable and cost-effective AI operations.
Following a Design Science Research approach, the study develops and evaluates a decision support prototype that consolidates energy- and operation-related information and supports the comparison of alternative configurations for AI inference workloads. The prototype aims to make relevant trade-offs transparent and provide actionable recommendations for energy-aware operational decisions. Its evaluation with practitioners indicates that the proposed approach can improve transparency and support decision-making in data center operations.
Based on the development and evaluation of the prototype, the paper derives a set of design principles for decision support systems that foster sustainable AI operations. The work contributes to research at the intersection of Information Systems, sustainable AI, and data center management and was conducted as part of the ESCADE project.
Reference:
Stein, H., Janzen, S., Agnes, C. K., Ninh, D. T., & Maass, W. (2026). Towards Decision Support Systems for Cost-Effective and Energy-Efficient AI Operations in Data Centers. In L. Fuentes, P. Plebani, C. Combi, & H. Reijers (Eds.), Advanced Information Systems Engineering: CAiSE 2026 (LNCS, Vol. 16558, pp. 394–413). Springer, Cham. https://doi.org/10.1007/978-3-032-28110-4_22
Contact:
Hannah Stein – hannah.stein@dfki.de