An Efficient Genetic Algorithm for Service Placement in Fog Computing

Dihia Bendjenahi, Chadia Moumeni, Malika Bessedik

2025

Abstract

The FSPP (Fog Service Placement Problem) involves the allocation of fog and cloud resources to IoT applications while meeting specific application requirements, including deadlines and minimizing response time. This paper addresses the FSPP in heterogeneous fog-cloud computing environments using a genetic algorithm (GA) approach. Our proposed GA aims to jointly maximize total resource utilization while respecting application deadlines. The paper presents a detailed system model, problem formulation, and the proposed GA methodology. Experimental results demonstrate the effectiveness of the GA approach in optimizing resource allocation and meeting Quality of Service (QoS) requirements when compared to the first-fit heuristic and to the random approach.

Paper Citation

in Harvard Style

Bendjenahi D., Moumeni C. and Bessedik M. (2025). An Efficient Genetic Algorithm for Service Placement in Fog Computing. In Proceedings of the 17th International Conference on Agents and Artificial Intelligence - Volume 3: ICAART; ISBN 978-989-758-737-5, SciTePress, pages 547-554. DOI: 10.5220/0013158900003890

in Bibtex Style

@conference{icaart25,
author={Dihia Bendjenahi and Chadia Moumeni and Malika Bessedik},
title={An Efficient Genetic Algorithm for Service Placement in Fog Computing},
booktitle={Proceedings of the 17th International Conference on Agents and Artificial Intelligence - Volume 3: ICAART},
year={2025},
pages={547-554},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0013158900003890},
isbn={978-989-758-737-5},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 17th International Conference on Agents and Artificial Intelligence - Volume 3: ICAART
TI - An Efficient Genetic Algorithm for Service Placement in Fog Computing
SN - 978-989-758-737-5
AU - Bendjenahi D.
AU - Moumeni C.
AU - Bessedik M.
PY - 2025
SP - 547
EP - 554
DO - 10.5220/0013158900003890
PB - SciTePress