Distributed Harmony: Intelligent Load Balancing in Heterogeneous Server Clusters Using Mobile Agents

Abstract

Intelligent network load balancing deals with efficiently distributing incoming network traffic among a collection of servers.
A mobile agent-based load balancing approach leverages intelligent agents to identify underutilized servers within a cluster and dynamically distribute incoming workloads to optimize resource allocation. Intelligent load balancing is crucial because most available network load balancing techniques are currently in use such as round robin, weighted round robin, least connection, and weighted least connect, and many others suffer from delay in response time, computational overhead, increased bandwidth usage, and traffic collision, and lack of distribution intelligence. An intelligent approach to network load balancing presents an algorithm that brings about equitable distribution of network traffic loads in cluster networks. Intelligent mobile agents use intelligent information to identify and generate responses on the availability and status of each server within the cluster and in turn equitably distribute the incoming network traffic to the available servers intelligently. Data was gathered using interviews, questionnaires, observations, and reviews of related literature. The analysis of the questionnaires was done using SPSS software. To assess the goodness of fit, a chi-square test was employed at the inferential level, providing statistical validation of the results. An object-oriented methodology was adopted. We also used the following tools: Java, MySQL, NVivo, and C#. The result clearly indicates that intelligent network load balancing techniques drastically reduce the response time delay and computational overhead usually experienced with other network load balancing techniques in use. It also facilitates the utilization of resources by providing a throughput with minimum response time, sharing the workload equally between the available servers depending on their status. Network traffic and bandwidth consumption are considerably reduced. The overall effect is that Internet usage is made more user-friendly both for professionals and nonprofessionals, thereby bringing about a reduction in cost.

Received: 6 January 2025 | Revised: 3 July 2025 | Accepted: 11 August 2025

Conflicts of Interest

The authors declare that they have no conflicts of interest to this work.

Data Availability Statement

The data that support this work are available upon reasonable request from the corresponding author.

Author Contribution Statement

John Ugah: Conceptualization, Methodology, Validation, Writing – original draft, Writing – review & editing, Visualization, Supervision, Project administration, Funding acquisition. Steven Ikporo: Software, Investigation, Data curation. Ifeanyi Achi: Software, Formal analysis, Investigation, Data curation. Ugochukwu Onwudebelu: Methodology, Formal analysis, Resources, Writing – original draft, Writing – review & editing, Visualization.