Performance of Load Balancing Algorithms on Homogeneous and Heterogeneous Servers in On-Premise Environments

Abstract

This research evaluates the performance of Round Robin, IP Hash, and Random Allocation algorithms in a homogeneous server environment, as well as Least Response Time, Least Connection, and Weighted Least Connection algorithms in a heterogeneous server environment implemented on on-premise servers. This study was motivated by the need to improve traffic management efficiency in local server infrastructure, where system performance is greatly influenced by resource diversity and distribution strategies. The experimental method was applied using NGINX and NGINX Plus as load balancing platforms, with Apache JMeter as a testing tool with low, medium, and high load test scenarios, while Netdata monitored the load distribution in real-time. Performance evaluation was based on six key metrics: throughput, latency, error rate, load distribution, CPU usage, and memory consumption. The results show that in a homogeneous environment, static algorithms such as Round Robin, IP Hash, and Random Allocation maintain stable performance with consistent throughput and minimal latency. Conversely, in a heterogeneous environment, dynamic algorithms, such as Weighted Least Connection, achieve lower latency and more balanced resource utilization. These findings highlight that algorithm selection must match system characteristics: static algorithms are more suitable for small-scale, uniform deployments, while dynamic approaches are recommended for heterogeneous or large-scale systems that require adaptive load management. Overall, weight-based dynamic approaches demonstrate superior scalability and resilience under high workloads.