Abstract

The article analyzes the feasibility of implementing machine learning in high-load web application architectures.  An architectural technique for integrating machine learning (ML) modules into microservice high-load Kubernetes-based web applications is considered. The basis is the "Sidecar + event-driven" pattern, which allows each service to be supplemented with load forecasting on LSTM models and anomaly detection through auto-encoders. The ML Predictor, Decision Manager, and Actuator components are embedded as separate containers that communicate via the Kafka event bus. The methodological basis of the work, which made it possible to broadly consider the features of the machine learning implementation process in high-load web application architectures, was based on the results of other studies. The information presented in the article is of considerable interest to architects of distributed systems and DevOps engineers specializing in building fault-tolerant, scalable high-traffic web platforms that need to integrate machine learning models without performance degradation and while maintaining SLA requirements. In addition, the materials of the article will be useful to researchers in the field of MLOps and data engineers involved in optimizing data pipelines and ensuring low latency of inference under extreme loads, as well as graduate students developing methods for adaptive resource balancing in heterogeneous computing environments.

Keywords

  • highly loaded web applications
  • auto-scaling
  • machine learning
  • load forecasting
  • anomaly detection
  • Kubernetes
  • Sidecar
  • event-driven

References

  1. 1. Mihai D. et al., “Integrated high-workload services for e-learning”, IEEE Access, Volume 11, pages 8441–8454, (2023).
  2. 2. Dakić V., Kovač M., Slovinac J., “Evolving High-Performance Computing Data Centers with Kubernetes, Performance Analysis, and Dynamic Workload Placement Based on Machine Learning Scheduling”, Electronics, Volume 13, Issue 13, pages 1–30, (2024).
  3. 3. Ferla D., “Enhancing Cloud Based Web Application Firewall with Machine Learning Models for Bot Detection and HTTP Traffic Classification”, Dissertation, Politecnico di Torino, pages 20–37, (2024).
  4. 4. Rabiman R., Nurtanto M., Kholifah N., “Design and Development E-Learning System by Learning Management System (LMS) in Vocational Education”, Online Submission, Volume 9, Issue 1, pages 1059–1063, (2020).
  5. 5. Gutu-Robu G. et al., “Cohesion network analysis: Customized curriculum management in Moodle”, Proceedings of the 22nd International Symposium on Symbolic and Numeric Algorithms for Scientific Computing (SYNASC), IEEE, pages 175–181, (2020).
  6. 6. Zhang X. et al., “Zeus: Improving resource efficiency via workload colocation for massive Kubernetes clusters”, IEEE Access, Volume 9, pages 105192–105204, (2021).
  7. 7. Lublinsky B., Jennings E., Spišaková V., “A Kubernetes ‘bridge’ operator between cloud and external resources”, Proceedings of the 2023 8th International Conference on Cloud Computing and Big Data Analytics (ICCCBDA), IEEE, pages 263–269, (2023).
  8. 8. Ding Z., Wang S., Jiang C., “Kubernetes-oriented microservice placement with dynamic resource allocation”, IEEE Transactions on Cloud Computing, Volume 11, Issue 2, pages 1777–1793, (2022).
  9. 9. Zhou N. et al., “Container orchestration on HPC systems”, Proceedings of the 2020 IEEE 13th International Conference on Cloud Computing (CLOUD), IEEE, pages 34–36, (2020).
  10. 10. Vasireddy I., Ramya G., Kandi P., “Kubernetes and docker load balancing: State-of-the-art techniques and challenges”, International Journal of Innovative Research in Engineering and Management, Volume 10, Issue 6, pages 49–54, (2023).