COMPARATIVE ANALYSIS OF PERFORMANCE AND EFFICIENCY OF LOAD BALANCING ALGORITHMS ON INGRESS CONTROLLER

Ahmad Rizal  Khamdani; Ahmad Rofiqul  Muslikh; Arif Saivul  Affandi

doi:10.52436/1.jutif.2025.6.1.4040

Ahmad Rizal Khamdani Information System, Faculty of Information Technology, University of Merdeka Malang, Indonesia
Ahmad Rofiqul Muslikh Information System, Faculty of Information Technology, University of Merdeka Malang, Indonesia
Arif Saivul Affandi Information System, Faculty of Information Technology, University of Merdeka Malang, Indonesia

DOI: https://doi.org/10.52436/1.jutif.2025.6.1.4040

Keywords: Container Orchestration, Ingress Controller, Kubernetes, Load Balancing, Load Testing

Abstract

Kubernetes has become the dominant container orchestration platform in production environments, with the ingress controller playing a critical role in managing external traffic to services within the cluster. This study aims to provide recommendations for optimal load balancing algorithms for Kubernetes production environments by analyzing and comparing the performance of four algorithms namely round robin, static-rr, least connection, and random on the HAProxy ingress controller. The research method is conducted through observation using k6 and Grafana performance test tools, as well as literature studies, with measurements including total requests, throughput, latency, CPU usage, and memory at various levels of user load. The data was analyzed using descriptive statistical techniques, normality test, homogeneity test, and tests for group differences using one-way ANOVA or Kruskal-Wallis H. The results show that static-rr excels in throughput, total requests, and CPU and memory efficiency at high load, while least connection is more effective for latency at low load. Round robin and random showed stable performance at low load but less optimal at high load. The conclusion of this study is that choosing the right load balancing algorithm depends on the load characteristics and desired performance metrics, to ensure optimal Kubernetes performance under various load scenarios in production environments.

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