Application of genetic algorithm to load balancing in networks with a homogeneous traffic flow

TL;DR

This paper proposes a genetic algorithm-based load balancing method for extended cloud networks, demonstrating improved performance over existing static solutions in dynamic traffic conditions.

Contribution

It introduces a novel genetic algorithm approach for dynamic load balancing in extended cloud networks, addressing limitations of static methods.

Findings

Genetic algorithm outperforms existing load balancing solutions.

Experimental results show improved network performance.

The method adapts effectively to changing traffic loads.

Abstract

The concept of extended cloud requires efficient network infrastructure to support ecosystems reaching form the edge to the cloud(s). Standard approaches to network load balancing deliver static solutions that are insufficient for the extended clouds, where network loads change often. To address this issue, a genetic algorithm based load optimizer is proposed and implemented. Next, its performance is experimentally evaluated and it is shown that it outperforms other existing solutions.