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

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)

Sinkron : Jurnal dan Penelitian Teknik Informatika
Vol. 10 No. 4 (2026): Research Articles October 2026

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.