Evaluating Kubernetes Autoscaling and Load Balancing to Improve Student Feedback Application Performance

Authors

  • Arvita Agus Kurniasari Department of Information Technology, State Polytechnic of Jember, Jember, Indonesia
  • Bety Etikasari Department of Information Technology, State Polytechnic of Jember, Jember, Indonesia
  • Aji Seto Arifianto Department of Information Technology, State Polytechnic of Jember, Jember, Indonesia
  • Lukie Perdanasari Politeknik Negeri Jember
  • Ahmad Fahriyannur Rosyady Department of Information Technology, State Polytechnic of Jember, Jember, Indonesia

DOI:

10.33395/sinkron.v10i4.16690

Keywords:

Autoscaling, Kubernetes, Load balancing, Resource management, Student feedback application

Abstract

The increasing use of digital platforms in higher education requires student feedback applications to maintain stable performance under growing workloads. This study evaluates Kubernetes autoscaling and load balancing through an integrated deployment in which multiple application pods, Kubernetes Service-based traffic distribution, Traefik routing, and Horizontal Pod Autoscaler configuration were enabled together. A conventional single-container deployment and the Kubernetes-based deployment ran on the same virtual machine and used the same intended duration-based workload pattern with a maximum of 294 virtual users. Each deployment scenario was evaluated through one independent workload execution. The results are interpreted as descriptive observations of the tested deployment configurations rather than statistically replicated experiments. Interval-level observations within each run were treated as temporally correlated measurements rather than independent experimental replications. Across the recorded runs, the Kubernetes-based deployment recorded 75.36% lower mean virtual-machine-level processor utilization, 95.87% lower mean interval-level p99 request duration, and 139.13% higher mean request rate than the single-container deployment.. For the retained workload-band time series above 200 virtual users, mean processor utilization was 99.22% for the single-container deployment and 39.53% for the Kubernetes-based deployment. The corresponding mean interval-level p99 values were 23.605 and 0.188 seconds, while mean request rates were 45.32 and 255.10 requests per second. The single-container deployment recorded failed-request rates of up to 3.14 requests per second in the retained time series, whereas no failed requests were recorded for the Kubernetes deployment. These results support the performance comparison of the complete configuration in which autoscaling and load-balancing mechanisms were enabled. However, replica-count, HPA-event, and per-pod traffic-distribution data were not collected, so the individual causal contributions of autoscaling and load balancing cannot be isolated.

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References

Alharthi, S., Alshamsi, A., Alseiari, A., & Alwarafy, A. (2024). Auto-scaling techniques in cloud computing: Issues and research directions. Sensors, 24(17), 5551. doi:10.3390/s24175551.

Augustyn, D. R., Wyciślik, Ł., & Sojka, M. (2024). Tuning a Kubernetes horizontal pod autoscaler for meeting performance and load demands in cloud deployments. Applied Sciences, 14(2), 646. doi:10.3390/app14020646.

Burns, B., Grant, B., Oppenheimer, D., Brewer, E., & Wilkes, J. (2016). Borg, Omega, and Kubernetes. Communications of the ACM, 59(5), 50–57. doi:10.1145/2890784.

Carrión, C. (2022). Kubernetes as a standard container orchestrator: A bibliometric analysis. Journal of Grid Computing, 20(4), 42. doi:10.1007/s10723-022-09629-8.

Chippagiri, S. (2024). Optimizing Kubernetes network performance: A study of container network interfaces and system tuning profiles. European Journal of Theoretical and Applied Sciences, 2(6), 651–668. doi:10.59324/ejtas.2024.2(6).58.

Creswell, J. W., & Creswell, J. D. (2023). Research design: Qualitative, quantitative, and mixed methods approaches (6th ed.). Thousand Oaks, CA: SAGE Publications.

Dogani, J., Namvar, R., & Khunjush, F. (2023). Auto-scaling techniques in container-based cloud and edge/fog computing: Taxonomy and survey. Computer Communications, 209, 120–150. doi:10.1016/j.comcom.2023.06.010.

Duman, C., & Eken, S. (2026). From reactive to predictive: A pattern-aware framework for Kubernetes autoscaling with large language model integration. Journal of Systems and Software, 237, 112861. doi:10.1016/j.jss.2026.112861.

Gurohman, D. T., Susanto, B. M., Hariyanto, A., Atmadji, E. S. J., Gumilang, M. A., Antika, E., & Mukhlisoh, N. A. (2024). Penerapan horizontal pod autoscaler dan Redis cluster berbasis Kubernetes untuk meningkatkan performa website e-learning. SKANIKA: Sistem Komputer dan Teknik Informatika, 7(2), 224–235. doi:10.36080/skanika.v7i2.3211.

Hadikusuma, R. S., Lukas, & Bachri, K. O. (2023). Survey paper: Optimization and monitoring of Kubernetes cluster using various approaches. Sinkron: Jurnal dan Penelitian Teknik Informatika, 8(3), 1357–1365. doi:10.33395/sinkron.v8i3.12424.

Joyce, J. E., & Sebastian, S. (2023). Enhancing Kubernetes auto-scaling: Leveraging metrics for improved workload performance. In 2023 Global Conference on Information Technologies and Communications (pp. 1–7). IEEE. doi:10.1109/GCITC60406.2023.10426170.

Martínez Saucedo, A., Rodríguez, G., Gomes Rocha, F., & Pereira dos Santos, R. (2025). Migration of monolithic systems to microservices: A systematic mapping study. Information and Software Technology, 177, 107590. doi:10.1016/j.infsof.2024.107590.

Mondal, S. K., Wu, X., Kabir, H. M. D., Dai, H.-N., Ni, K., Yuan, H., & Wang, T. (2023). Toward optimal load prediction and customizable autoscaling scheme for Kubernetes. Mathematics, 11(12), 2675. doi:10.3390/math11122675.

Nguyen, Q.-M., Phan, L.-A., & Kim, T. (2022). Load-balancing of Kubernetes-based edge computing infrastructure using resource adaptive proxy. Sensors, 22(8), 2869. doi:10.3390/s22082869.

Nguyen, T. T., Yeom, Y. J., Kim, T., Park, D. H., & Kim, S. (2020). Horizontal pod autoscaling in Kubernetes for elastic container orchestration. Sensors, 20(16), 4621. doi:10.3390/s20164621.

Niazi, M., Abbas, S., Soliman, A.-H., Alyas, T., Asif, S., & Faiz, T. (2023). Vertical pod autoscaling in Kubernetes for elastic container collaborative framework. Computers, Materials & Continua, 74(1), 591–606. doi:10.32604/cmc.2023.032474.

Pradipta, K. S., Saskara, G. A. J., & Yudistira, B. G. K. (2026). Implementasi Kubernetes cluster untuk high availability dan load balancing SIAK Undiksha. RIGGS: Journal of Artificial Intelligence and Digital Business, 4(4), 12831–12840. doi:10.31004/riggs.v4i4.5846.

Sujarwo, M. A. I. F. P., Istikmal, I., & Irawan, A. I. (2023). Analysis of load balancing least connection and shortest expected delay algorithm for web server using kube-proxy on Kubernetes. ELKOMIKA: Jurnal Teknik Energi Elektrik, Teknik Telekomunikasi, & Teknik Elektronika, 11(2), 439. doi:10.26760/elkomika.v11i2.439.

Tran, M.-N., Vu, D.-D., & Kim, Y. (2022). A survey of autoscaling in Kubernetes. In 2022 Thirteenth International Conference on Ubiquitous and Future Networks (pp. 263–265). IEEE. doi:10.1109/ICUFN55119.2022.9829572.

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How to Cite

Kurniasari, A. A. ., Etikasari, B., Seto Arifianto, A., Perdanasari, L., & Rosyady, A. F. . (2026). Evaluating Kubernetes Autoscaling and Load Balancing to Improve Student Feedback Application Performance. Sinkron : Jurnal Dan Penelitian Teknik Informatika, 10(4), 2087-2099. https://doi.org/10.33395/sinkron.v10i4.16690

Issue

Vol. 10 No. 4 (2026): Research Articles October 2026

Section

Articles

License

Copyright (c) 2026 Arvita Agus Kurniasari, Bety Etikasari, Aji Seto Arifianto, Lukie Perdanasari, Ahmad Fahriyannur Rosyady

Creative Commons License

This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.

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