Machine Learning Applications in Kubernetes for Autonomous Container Management
Keywords:
Kubernetes, Machine Learning, Autonomous Management, Container Orchestration, Auto-scaling, Resource Optimization, Predictive Analytics, AI-driven Automation, Performance Tuning, Cluster ManagementAbstract
This study investigates the incorporation of machine learning (ML) into Kubernetes to improve autonomous container management. Our primary focus is to explore the potential of machine learning in enhancing predictive auto-scaling, resource optimization, and self-healing capabilities in Kubernetes environments. This study thoroughly examines current research, consolidates significant discoveries, and highlights developing patterns. Our approach thoroughly examines various secondary data sources, such as academic articles, industry reports, and case studies. Our research has uncovered some fascinating insights into the impact of machine learning on predictive analytics and resource management in Kubernetes clusters. The results show a clear improvement in performance, efficiency, and reliability. In addition, techniques for autonomously detecting and resolving anomalies can help minimize downtime and operational disruptions. Nevertheless, there are still obstacles to overcome, including ensuring data quality, managing computational overhead, and addressing the demand for explainable AI. Policy implications involve strong data governance, transparent AI decision-making, and investment in scalable infrastructure. This study suggests that ML applications in Kubernetes have the potential to bring about significant changes, leading to more intelligent, robust, and efficient cloud-native operations. Organizations can maximize the advantages of autonomous container management in Kubernetes environments by acknowledging current constraints and embracing new developments such as federated learning and multi-cloud orchestration.
References
Augustyn, D. R., Wycislik, L., Sojka, M. (2024). Tuning a Kubernetes Horizontal Pod Autoscaler for Meeting Performance and Load Demands in Cloud Deployments. Applied Sciences, 14(2), 646. https://doi.org/10.3390/app14020646
Cai, H., Wang, C., Zhou, X. (2021). Deployment and Verification of Machine Learning Tool-chain Based on Kubernetes Distributed Clusters. CCF Transactions on High Performance Computing, 3(2), 157-170. https://doi.org/10.1007/s42514-021-00065-w
Chen, T., Bahsoon, R., & Buyya, R. (2019). A Reinforcement Learning-Based Approach to Autonomous Autoscaling for Cloud-Based Services. Proceedings of the ACM Symposium on Cloud Computing, 329-340. https://doi.org/10.1145/3292500.3330940
Deng, L., Wang, Z., Sun, H., Li, B., Yang, X. (2023). A Deep Reinforcement Learning-based Optimization Method for Long-running Applications Container Deployment. International Journal of Computers, Communications and Control, 18(4). https://doi.org/10.15837/ijccc.2023.4.5013
Donca, I-C., Stan, O. P., Misaros, M., Stan, A., Miclea, L. (2024). Comprehensive Security for IoT Devices with Kubernetes and Raspberry Pi Cluster. Electronics, 13(9), 1613. https://doi.org/10.3390/electronics13091613
Du, T., Xiao, G. Y., Chen, J., Zhang, C. F., Sun, H. (2023). A Combined Priority Scheduling Method for Distributed Machine Learning. EURASIP Journal on Wireless Communications and Networking, 2023(1), 45. https://doi.org/10.1186/s13638-023-02253-4
Fu, J., Zhang, H., & Liu, Y. (2022). Ensemble Deep Learning for Time Series Forecasting: A Survey. Journal of Parallel and Distributed Computing, 162, 47-61. https://doi.org/10.1016/j.jpdc.2022.03.009
Hirata, E., Hansen, A. S. (2024). Identifying Key Issues in Integration of Autonomous Ships in Container Ports: A Machine-Learning-Based Systematic Literature Review. Logistics, 8(1), 23. https://doi.org/10.3390/logistics8010023
Ji-Beom, K., Choi, J-B., Eun-Sung, J. (2024). Design and Implementation of an Automated Disaster-Recovery System for a Kubernetes Cluster Using LSTM. Applied Sciences, 14(9), 3914. https://doi.org/10.3390/app14093914
Kim, Y., Kim, J., & Chung, J. (2018). Resource Management for Containerized Cloud Applications: A Machine Learning Approach. Proceedings of the IEEE International Conference on Autonomic Computing, 150-159. https://doi.org/10.1109/ICAC.2018.00022
Lee, P., Theotokatos, G., Boulougouris, E. (2024). Robust Decision-Making for the Reactive Collision Avoidance of Autonomous Ships against Various Perception Sensor Noise Levels. Journal of Marine Science and Engineering. 12(4), 557. https://doi.org/10.3390/jmse12040557
Li, X., Zhao, J., & Wang, Y. (2018). Predictive Resource Scaling for Cloud Applications. IEEE International Conference on Distributed Computing Systems, 412-420. https://doi.org/10.1109/ICDCS.2018.00139
Lipton, Z. C. (2018). The Mythos of Model Interpretability. Proceedings of the 2016 ICML Workshop on Human Interpretability in Machine Learning, 10-18. https://doi.org/10.1145/3287560.3287596
Meng, X., Wang, S., & Chen, Y. (2018). Auto-scaling Microservices: A Dynamic Resource Management Model Based on Machine Learning. IEEE International Conference on Distributed Computing Systems, 211-220. https://doi.org/10.1109/ICDCS.2018.00120
Senjab, K., Abbas, S., Ahmed, N., Khan, A. U. R. (2023). A Survey of Kubernetes Scheduling Algorithms. Journal of Cloud Computing, 12(1), 87. https://doi.org/10.1186/s13677-023-00471-1
Ullah, A., Kiss, T., Kovács, J., Tusa, F., Deslauriers, J. (2023). Orchestration in the Cloud-to-Things Compute Continuum: Taxonomy, Survey and Future Directions. Journal of Cloud Computing, 12(1), 135. https://doi.org/10.1186/s13677-023-00516-5
Weiss, E., Caplan, S., Horn, K., Sharabi, M. (2024). Real-Time Defect Detection in Electronic Components during Assembly through Deep Learning. Electronics, 13(8), 1551. https://doi.org/10.3390/electronics13081551
Xu, Q., Li, J., & Zhao, Y. (2019). Reinforcement Learning for Resource Management in Cloud Data Centers. IEEE International Conference on Cloud Computing Technology and Science, 215-224. https://doi.org/10.1109/CloudCom.2019.00026
Yuan, H., Liao, S. (2024). A Time Series-Based Approach to Elastic Kubernetes Scaling. Electronics, 13(2), 285. https://doi.org/10.3390/electronics13020285
Zhu, L., Huang, K., Fu, K., Hu, Y., Wang, Y. (2023). A Priority-aware Scheduling Framework for Heterogeneous Workloads in Container-based Cloud. Applied Intelligence, 53(12), 15222-15245. https://doi.org/10.1007/s10489-022-04164-1
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