ERDQ-LEARNING: MÔ HÌNH HỌC TĂNG CƯỜNG TÍCH HỢP HPA PHÂN CHIA TỶ LỆ POD SERVERLESS TRONG KUBERNETES

Đặng Thanh Chương và CS. Tập 134, Số 2A, 2025 162 Tài liệu tham khảo 1. Wen, J., Liu, Y., Chen, Z., Chen, J., & Ma, Y., "Characterizing commodity serverless computing platforms," Journal of Software: Evolution and Process, vol. 35, no. 10, p. 2394, 2023. 2. Wen, J., Chen, Z., Jin, X., & Liu, X., "Rise of the planet of serverless computing: A systematic review," CM Transactions on Software Engineering and Methodology, vol. 32, no. 5, pp. 1-61, 2023. 3. Priscilla Benedetti, Mauro Femminella, Gianluca Reali, and Kris Steenhaut, "Reinforcement learning applicability for resource-based auto-scaling in serverless edge applications," In 2022 IEEE International Conference on Pervasive Computing and Communications Workshops and other Affiliated Events (PerCom Workshops), p. 674–679, IEEE, 2022. 4. Tari, M., Ghobaei-Arani, M., Pouramini, J., & Ghorbian, M., "Auto-scaling mechanisms in serverless computing: A comprehensive review," Computer Science Review, vol. 53, pp. 53,100650, 2024. 5. J. Spillner, C. Mateos and D. A. Monge, "FaaSification of applications – From monolithic to serverless container-based architectures," in Cloud Computing and Services Science, Springer, 2019, p. 131–151. 6. K. Team, "Request flow in Knative Serving," 06 2025. [Online]. Available: https://knative.dev/docs/serving/request-flow/. 7. R. Srilakshmi, "Scaling Applications using HPA in Azure DevOps and Kubernetes," 2024. [Online]. Available: https://www.linkedin.com/pulse/scaling-applications-using-hpa-azure-devops-kubernetes- srilakshmi-r-upzfc.

8. "Kubernetes horizontal pod autoscaler," https://kubernetes.io/docs/tasks/run-application/horizontal- pod-autoscale/, [Online]. Available: https://kubernetes.io/docs/tasks/run-application/horizontal-pod- autoscale/. [Accessed 02 2025]. 9. Zhiyu Zhang, Tao Wang, An Li, and Wenbo Zhang, "Adaptive auto-scaling of delaysensitive serverless services with reinforcement learning," In 2022 IEEE 46th Annual Computers, Software, and Applications Conference (COMPSAC), p. 866–871, IEEE, 2022. 10. W. M. A. P. C. W. H. F. Z. T. K. T. B. a. R. K. I. H. Qiu, "Reinforcement learning for resource management in multi-tenant serverless platforms," in Systems, Proc. of the 2nd European Workshop on Machine Learning and, 2022, p. 20–28. 11. M. J. a. R. H. F. Rossi, "Efficient resource allocation using deep reinforcement learning," in Proc. 20th Int. Conf. on Machine Learning and Applications (ICMLA), 2021, pp. 811-816. 12. A. A. K. a. I. Ra, "Intelligent autoscaling of microservices in the cloud for real-time applications," IEEE Access, vol. 9, p. 35464–35476, 2021. 13. L. Zhong, "Reinforcement Learning Based Resource Allocation Mechanisms in Serverless Clouds," Amsterdam, 2023. 14. J. P. P. dos Santos, T. Wauters, B. Volckaert and F. De Turck, "Gym-HPA: Efficient Auto-Scaling via Reinforcement Learning for Complex Microservice-based Applications in Kubernetes," Miami, FL, USA, 2023. 15. K. K. D. S. A. G. I. A. D. W. a. M. R. V. Mnih, "Human-level control through deep reinforcement learning," Nature, vol. 518, no. 7540, p. 529–533, 2015.