KAN-GRU: A Lightweight Temporal Kolmogorov-Arnold Network for Cloud Resource Utilization Forecasting
Abstract
Accurate forecasting of cloud resource utilization is essential for proactive provisioning, autoscaling, and energy-aware scheduling in modern data centers. Temporal Kolmogorov-Arnold Networks (TKANs) provide a promising alternative to MLP-based models by combining Kolmogorov- Arnold representations with recurrent sequential modeling. However, prior TKAN formulation typically relies on LSTM-style recurrence, which increases parameter count and computational cost. This work introduces KAN–GRU, a lightweight TKAN-based architecture that substitutes the LSTM cell in the TKAN temporal component with a GRU, reducing parameters while preserving temporal modeling capacity. The evaluation benchmarks KAN–GRU for data-center resource-utilization forecasting under univariate and multivariate configurations and across single-step and multi-step prediction horizons. The results indicate that KAN-GRU improves computational efficiency while maintaining competitive predictive performance, making it suitable for scalable deployment in monitoring and control operations in cloud environments.
Keywords
- Cloud resources
- Gated Recurrent Unit (GRU)
- Kolmogorov-Arnold Networks (KAN)
- Temporal/Recurrent KAN
- Utilization forecasting
Project Number
Ethical Statement
References
- A. Katal, S. Dahiya, and T. Choudhury, “Energy efficiency in cloud computing data centers: A survey on software technologies,” Cluster Computing, vol. 26, no. 3, pp. 1845–1875, 2023, doi: 10.1007/s10586-022-03713-0.
- S. M. Sajal et al., “Kerveros: Efficient and scalable cloud admission control,” in Proc. 17th USENIX Symp. Oper. Syst. Des. Implement. (OSDI), Boston, MA, USA, July 2023, pp. 227–245.
- H. Yuan, J. Bi, S. Li, J. Zhang, and M. Zhou, “An improved LSTM-based prediction approach for resources and workload in large-scale data centers,” IEEE Internet of Things Journal, vol. 11, no. 12, pp. 22816–22829, June 2024, doi: 10.1109/JIOT.2024.3383512.
- G. Marques, C. R. Senna, S. Sargento, L. Carvalho, L. Pereira, and R. Matos, “Proactive resource management for cloud of services environments,” Future Generation Computer Systems, vol. 150, pp. 90–102, Jan. 2024, doi: 10.1016/j.future.2023.08.005.
- D. Saxena, J. Kumar, A. K. Singh, and S. Schmid, “Performance analysis of machine learning centered workload prediction models for cloud,” IEEE Transactions on Parallel and Distributed Systems, vol. 34, no. 4, pp. 1313–1330, Apr. 2023, doi: 10.1109/TPDS.2023.3240567.
- M. P. Yadav, N. Pal, and D. K. Yadav, “Workload prediction over cloud server using time series data,” in Proc. 11th International Conference on Cloud Computing, Data Science & Engineering (Confluence), Noida, India, Jan. 2021, pp. 267–272, doi: 10.1109/Confluence51648.2021.9377032.
- Y. Dang, Q. Lin, and P. Huang, “AIOps: Real-world challenges and research innovations,” in Proc. 2019 IEEE/ACM 41st International Conference on Software Engineering: Companion Proceedings (ICSE-Companion), Montreal, QC, Canada, May 2019, pp. 4–5, doi: 10.1109/ICSE-Companion.2019.00023.
- S. Malik, M. Tahir, M. Sardaraz, and A. Alourani, “A resource utilization prediction model for cloud data centers using evolutionary algorithms and machine learning techniques,” Applied Sciences, vol. 12, no. 4, Art. no. 2160, 2022, doi: 10.3390/app12042160.
Details
Primary Language
English
Subjects
Artificial Intelligence (Other)
Journal Section
Research Article
Authors
Ezgi Nur Alışan
0009-0003-9280-1074
Türkiye
Emre Sefer
0000-0002-9186-0270
Türkiye
İsmail Aktürk
*
0000-0003-1970-2507
Türkiye
Early Pub Date
October 27, 2026
Publication Date
October 30, 2026
Submission Date
April 30, 2026
Acceptance Date
August 31, 2026
Published in Issue
Year 2026 Volume: 9 Number: 5