Load Balancing Optimization in Cloud Task Scheduling Using Genetic Algorithm

Fellycia Caroline; Yohannes

doi:10.59934/jaiea.v5i3.2382

Authors

Fellycia Caroline Universitas Multi Data Palembang
Yohannes Universitas Multi Data Palembang

DOI:

https://doi.org/10.59934/jaiea.v5i3.2382

Keywords:

Cloud Computing, Genetic Algorithm, Load Balancing, Task Scheduling, Virtual Machine

Abstract

Cloud computing environments face significant challenges in task scheduling and load balancing due to the increasing scale and complexity of computing workloads. Inefficient task scheduling leads to uneven resource utilization, increased makespan, and higher operational costs. This research proposes load balancing optimization in cloud task scheduling using a Genetic Algorithm applied to the Cloud Task Scheduling Dataset. The dataset underwent preprocessing including categorical encoding, data cleaning, and Min-Max Normalization prior to the optimization process. The Genetic Algorithm was implemented using Tournament Selection, Two-Point Crossover, and Uniform Integer Mutation, with the fitness function formulated based on makespan and degree of load imbalance minimization. The performance of the proposed approach was evaluated against a Random Assignment baseline across five metrics: makespan, degree of load imbalance, load distribution efficiency, average per-task completion time, and computational cost. The results demonstrated that the Genetic Algorithm significantly outperformed, achieving a makespan reduction of 51.33%, a load imbalance reduction of 43.81%, a load distribution efficiency improvement of 11.24%, an average per-task completion time reduction of 26.37%, and a computational cost reduction of 19.70%. These findings confirm that the Genetic Algorithm is an effective approach for optimizing task scheduling and load balancing in cloud computing environments.

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