Location via proxy:   [ UP ]  
[Report a bug]   [Manage cookies]                
Skip to main content

Advertisement

Springer Nature Link
Log in

Adaptive horizontal scaling in kubernetes clusters with ANN-based load forecasting

  • Published:
Cluster Computing Aims and scope Submit manuscript

Abstract

In modern cloud environments, efficient management of computational resources is a critical challenge due to the growing demand for scalable and high-performance applications. Horizontal scaling in Kubernetes (K8s) clusters is essential for dynamically adjusting resources to match workload demands. However, their reactive nature often limits traditional autoscaling methods like K8s Horizontal Pod Autoscaler (HPA), leading to inefficiencies under variable loads. To overcome these limitations, more advanced and adaptive scaling approaches are needed. Thus, this study introduces an adaptive approach to horizontal scaling in K8s clusters using Artificial Neural Networks (ANNs) for load forecasting, referred to as ANN-HS. The proposed method aims to enhance the efficiency of resource consumption and optimize replica allocation compared to the standard HPA. By leveraging pre-trained regression models, ANN-HS dynamically adjusts resources to meet varying demands, ensuring adherence to latency requirements and improving overall system performance. Experimental results demonstrate that ANN-HS outperforms traditional HPA methods, offering a scalable and flexible solution for managing microservices in cloud environments. This approach provides a robust framework for optimizing horizontal scaling in Kubernetes, contributing to the advancement of intelligent resource management in cluster computing. Experimental results show that ANN-HS significantly improves resource utilization compared to Kubernetes’ HPA. Specifically, ANN-HS reduces CPU consumption by approximately 50% while maintaining Service Level Agreement (SLA) compliance with an average violation rate of less than 10%. Additionally, ANN-HS reduces the number of replicas needed by 66.67%, optimizing resource allocation under varying load conditions.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12
Fig. 13

Similar content being viewed by others

Data availability

The datasets generated and/or analysed during the current study are available in the Mendeley Data repository, https://data.mendeley.com/datasets/ks9vbv5pb2/1.

Materials availibility

Not applicable

Code availibility

Not applicable

References

  1. Tran, M.-N., Vu, D.-D., Kim, Y.: A survey of autoscaling in kubernetes. In: 2022 Thirteenth International Conference on Ubiquitous and Future Networks (ICUFN), pp. 263–265 (2022). IEEE

  2. Huo, Q., Li, S., Xie, Y., Li, Z.: Horizontal pod autoscaling based on kubernetes with fast response and slow shrinkage. In: 2022 International Conference on Artificial Intelligence, Information Processing and Cloud Computing (AIIPCC), pp. 203–206 (2022). IEEE

  3. Kuranage, M.P.J., Hanser, E., Nuaymi, L., Bouabdallah, A., Bertin, P., Al-Dulaimi, A.: Ai-assisted proactive scaling solution for cnfs deployed in kubernetes. In: 2023 IEEE 12th International Conference on Cloud Networking (CloudNet), pp. 265–273 (2023). IEEE

  4. Augustyn, D.R., Wyciślik, Ł, Sojka, M.: Tuning a kubernetes horizontal pod autoscaler for meeting performance and load demands in cloud deployments. Appl. Sci. 14(2), 646 (2024)

    Article  MATH  Google Scholar 

  5. Senjab, K., Abbas, S., Ahmed, N., Khan, A.U.R.: A survey of kubernetes scheduling algorithms. J. Cloud Comp. 12(1), 87 (2023)

    Article  MATH  Google Scholar 

  6. Zafeiropoulos, A., Fotopoulou, E., Filinis, N., Papavassiliou, S.: Reinforcement learning-assisted autoscaling mechanisms for serverless computing platforms. Sim. Modell. Prac. Theory 116, 102461 (2022)

    Article  Google Scholar 

  7. Tamiru, M.A., Tordsson, J., Elmroth, E., Pierre, G.: An experimental evaluation of the kubernetes cluster autoscaler in the cloud. In: 2020 IEEE International Conference on Cloud Computing Technology and Science (CloudCom), pp. 17–24 (2020). https://doi.org/10.1109/CloudCom49646.2020.00002

  8. Balla, D., Simon, C., Maliosz, M.: Adaptive scaling of kubernetes pods. In: NOMS 2020 - 2020 IEEE/IFIP Network Operations and Management Symposium, pp. 1–5 (2020). https://doi.org/10.1109/NOMS47738.2020.9110428

  9. Nguyen, H.T., Van Do, T., Rotter, C.: Scaling upf instances in 5g/6g core with deep reinforcement learning. IEEE Access 9, 165892–165906 (2021)

    Article  Google Scholar 

  10. Horizontal Pod Autoscaling. https://kubernetes.io/docs/tasks/run-application/horizontal-pod-autoscale/. Accessed: 2024-06-13 (2024). https://kubernetes.io/docs/tasks/run-application/horizontal-pod-autoscale/

  11. Nguyen, T.-T., Yeom, Y.-J., Kim, T., Park, D.-H., Kim, S.: Horizontal pod autoscaling in kubernetes for elastic container orchestration. Sensors (2020). https://doi.org/10.3390/s20164621

    Article  MATH  Google Scholar 

  12. Shim, S., Dhokariya, A., Doshi, D., Upadhye, S., Patwari, V., Park, J.-Y.: Predictive auto-scaler for kubernetes cloud. In: 2023 IEEE International Systems Conference (SysCon), pp. 1–8 (2023). https://doi.org/10.1109/SysCon53073.2023.10131106

  13. Silva, S.N., Goldbarg, M.A.S.d.S., Silva, L.M.D.d., Fernandes, M.A.C.: Application of fuzzy logic for horizontal scaling in kubernetes environments within the context of edge computing. Future Internet 16(9) (2024) https://doi.org/10.3390/fi16090316

  14. Khaleq, A.A., Ra, I.: Intelligent autoscaling of microservices in the cloud for real-time applications. IEEE Access 9, 35464–35476 (2021)

    Article  MATH  Google Scholar 

  15. Toka, L., Dobreff, G., Fodor, B., Sonkoly, B.: Adaptive ai-based auto-scaling for kubernetes. In: 2020 20th IEEE/ACM International Symposium on Cluster, Cloud and Internet Computing (CCGRID), pp. 599–608 (2020). https://doi.org/10.1109/CCGrid49817.2020.00-33

  16. Yuan, H., Liao, S.: A time series-based approach to elastic kubernetes scaling. Electronics (2024). https://doi.org/10.3390/electronics13020285

    Article  MATH  Google Scholar 

  17. Dang-Quang, N.-M., Yoo, M.: Deep learning-based autoscaling using bidirectional long short-term memory for kubernetes. Appl. Sci. (2021). https://doi.org/10.3390/app11093835

    Article  MATH  Google Scholar 

  18. Yan, M., Liang, X., Lu, Z., Wu, J., Zhang, W.: Hansel: Adaptive horizontal scaling of microservices using bi-lstm. Appl. Soft Comp. 105, 107216 (2021). https://doi.org/10.1016/j.asoc.2021.107216

    Article  MATH  Google Scholar 

  19. Violos, J., Tsanakas, S., Theodoropoulos, T., Leivadeas, A., Tserpes, K., Varvarigou, T.: Intelligent horizontal autoscaling in edge computing using a double tower neural network. Comp. Netw. 217, 109339 (2022). https://doi.org/10.1016/j.comnet.2022.109339

    Article  Google Scholar 

  20. Zerwas, J., Krämer, P., Ursu, R.-M., Asadi, N., Rodgers, P., Wong, L., Kellerer, W.: KapetÁnios: Automated kubernetes adaptation through a digital twin. In: 2022 13th International Conference on Network of the Future (NoF), pp. 1–3 (2022). https://doi.org/10.1109/NoF55974.2022.9942649

  21. Toka, L., Dobreff, G., Fodor, B., Sonkoly, B.: Machine learning-based scaling management for kubernetes edge clusters. IEEE Trans. Netw. Ser. Manag. 18(1), 958–972 (2021). https://doi.org/10.1109/TNSM.2021.3052837

    Article  Google Scholar 

  22. MicroK8s: Lightweight Kubernetes. https://microk8s.io/. Acesso em: 18-07-2023

  23. The Apache Software Foundation: Apache JMeter. https://jmeter.apache.org/. Acesso em: 18-07-2023 (2023)

  24. The Prometheus Authors: Prometheus. https://prometheus.io/. Acesso em: 18-07-2023 (2023)

  25. Fernandes, M.: Horizontal Scaling in Kubernetes Dataset Using Artificial Neural Networks for Load Forecasting (2024). https://doi.org/10.17632/ks9vbv5pb2.1

  26. Red Hat: Fabric8 Kubernetes-Client. https://github.com/fabric8io/kubernetes-client. Acesso em: 18-07-2023 (2023)

  27. Xiao, Z., Hu, S.: Dscaler: A horizontal autoscaler of microservice based on deep reinforcement learning. In: 2022 23rd Asia-Pacific Network Operations and Management Symposium (APNOMS), pp. 1–6 (2022). https://doi.org/10.23919/APNOMS56106.2022.9919994

Download references

Acknowledgements

The authors would like to express their gratitude to the Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES) for providing financial support. This research was also made possible with the support of the InovAI Laboratory at UFRN and the Santa Cruz Campus of IFRN.

Funding

Not applicable

Author information

Authors and Affiliations

  1. InovAI Lab, nPITI/IMD, Federal University of Rio Grande do Norte (UFRN), Natal, 59078-900, RN, Brazil

    Lucileide M. D. da Silva, Pedro V. A. Alves, Sérgio N. Silva & Marcelo A. C. Fernandes

  2. Leading Advanced Technologies Center of Excellence (LANCE), nPITI/IMD, UFRN, Natal, 59078-900, RN, Brazil

    Lucileide M. D. da Silva, Pedro V. A. Alves, Sérgio N. Silva & Marcelo A. C. Fernandes

  3. Federal Institute of Education, Science and Technology of Rio Grande do Norte (IFRN), Santa Cruz, 59200-000, RN, Brazil

    Lucileide M. D. da Silva

  4. Department of Computer and Automation Engineering, UFRN, Natal, 59078-900, RN, Brazil

    Marcelo A. C. Fernandes

Authors
  1. Lucileide M. D. da Silva
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Pedro V. A. Alves
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Sérgio N. Silva
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Marcelo A. C. Fernandes
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

All authors have contributed in various degrees to ensure the quality of this work (e.g., L.M.D.d.S., P.V.A.A., S.N.S, and M.A.C.F. conceived the idea and experiments; L.M.D.d.S., P.V.A.A., S.N.S, and M.A.C.F. designed and performed the experiments; L.M.D.d.S., P.V.A.A., S.N.S., and M.A.C.F. analyzed the data; L.M.D.d.S., S.N.S, and M.A.C.F wrote the paper. L.M.D.d.S and M.A.C.F. coordinated the project). All authors have read and agreed to the published version of the manuscript.

Corresponding author

Correspondence to Marcelo A. C. Fernandes.

Ethics declarations

Conflict of interest

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Ethical approval

Not applicable

Consent to participate

Not applicable

Consent for publication

All authors agreed with the content and gave explicit consent to submit.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

da Silva, L.M.D., Alves, P.V.A., Silva, S.N. et al. Adaptive horizontal scaling in kubernetes clusters with ANN-based load forecasting. Cluster Comput 28, 176 (2025). https://doi.org/10.1007/s10586-024-04887-5

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1007/s10586-024-04887-5

Keywords

Search

Navigation