Journal of Soft Computing and Information Technology

Journal of Soft Computing and Information Technology

A Multi-Stage Hybrid Optimization Model Based on IJFSA for Load Balancing and Performance Enhancement in Fog–Cloud Environments

Document Type : Persian Original Article

Authors
Sakine Sohrabi
Reza Mohammadi
Mehdi Sakhaei-nia
Mohammad Nassiri
Computer Engineering Department, Bu-Ali Sina University, Hamedan, Iran.
10.22034/jscit.2026.560929.2194
Abstract
- In recent years, emerging technologies have transformed human interaction with digital systems. The integration of the Internet of Things (IoT) with cloud infrastructures has enabled intelligent and automated services, particularly in healthcare. The growing number of networked sensors generates massive volumes of real-time data, increasing the demand for fast processing. Reliance on remote data centers, however, can lead to latency and reduced efficiency in time-sensitive systems. In this context, fog computing, as an intermediate layer between edge devices and the cloud, provides an effective solution and highlights the need for intelligent optimization algorithms. In this study, the IJFSA-ODA algorithm is introduced as an improved version of the Jellyfish algorithm that enhances search performance through the purposeful integration of several metaheuristic operators. Specifically, an operator inspired by Differential Evolution is employed to increase diversity, a mechanism inspired by the Artificial Bee Colony algorithm is used to strengthen exploitation, and an operator inspired by Simulated Annealing is applied to prevent premature convergence. Simulation results demonstrate that the proposed algorithm exhibits competitive performance compared with IJFSA, IJFA, PSO-SA, and PSO, and achieves acceptable improvements in key performance metrics under certain scenarios. For a scenario involving 25 virtual machines and up to 2000 tasks, the results indicate a relative reduction of approximately 5–10% in total processing time and 5–12% in energy consumption compared to PSO-based methods. From the perspective of load balancing, the results of Jain’s Fairness Index indicate that although the index does not reach its ideal value (close to 1) for any of the evaluated algorithms, the proposed algorithm achieves higher fairness values than the reference methods in most scenarios, reflecting a more balanced workload distribution among virtual machines. Furthermore, the results show that the performance of the IJFSA-ODA algorithm remains stable as the number of tasks and nodes increases, exhibiting lower fluctuations in the deadline-meet ratio compared to some baseline algorithms. In addition, the results obtained from 10 independent runs confirm the acceptable statistical stability and reliability of the proposed algorithm.
Keywords
Load Balancing
Task Scheduling
IJFSA
Artificial Bee Colony
Evolutionary Algorithm
Energy Consumption
[1] Boubaker, N. E. H., Zarour, K., Guermouche, N., & Benmerzoug, D. (2025). A comprehensive survey on resource management for IoT applications in edge–fog–cloud environments. IEEE Access.
[2] Sohrabi, S., Sakhaei-nia, M., Nassiri, M., & Mohammadi, R. (2025). IJFSA-OPG: A hybrid energy efficient load-balancing mechanism for fog-based IoT healthcare. Manuscript submitted for publication, Cloud Computing Journal.
[3] Ghorbian, Mohsen, Mostafa Ghobaei-Arani, and Leila Esmaeili. "A survey on the scheduling mechanisms in serverless computing: a taxonomy, challenges, and trends." Cluster Computing (2024): 1-40.‏
[4] Mokni, I., & Yassa, S. (2024). A multi-objective approach for op-timizing IoT applications offloading in fog–cloud environments with NSGA-II. The Journal of Supercomputing, 1-39.‏
[5] Aghazadeh, Rafat, Ali Shahidinejad, and Mostafa Ghobaei‐Arani. "Proactive content caching in edge computing environment: A re-view." Software: Practice and Experience 53.3 (2023): 811-855.‏
[6] Bezdan, T.; Zivkovic, M.; Bacanin, N.; Strumberger, I.; Tuba, E.; Tuba, M. Multi-objective task scheduling in a cloud computing environment by hybridized bat algorithm. J. Intell. Fuzzy Syst. 2022, 42, 411–423
[7] Available online: https://eucloudedgeiot.eu/ (accessed on 14 Feb-ruary 2023).
[8] Ghorbian, Mohsen, Mostafa Ghobaei-Arani, and Rohollah Asado-lahpour-Karimi. "Function Placement Approaches in Serverless Computing: A Survey." Journal of Systems Architecture (2024): 103291.‏
[9] Abohamama, A.S.; El-Ghamry, A.; Hamouda, E. Real-time task scheduling algorithm for IoT-based applications in the cloud–fog environment. J. Netw. Syst. Manag. 2022, 30, 1–35.
[10] Zhang, Y., et al. (2022). "Task Scheduling in Cloud Computing Environment Using Advanced Phasmatodea Population Evolution Algorithm." MDPI Electronics, 11(9), 1451. DOI: 10.3390/electronics11091451MDPI
[11] Kumar, R., et al. (2022). "An Efficient Task Scheduling in a Cloud Computing Environment Using Hybrid Genetic Algorithm - Parti-cle Swarm Optimization (GA-PSO) Algorithm." World Scientific Journal, 28(3), 1850024. DOI: 10.1142/S0219622018500244ResearchGate+1
[12] Chen, W., et al. (2021). "Blockchain-Enhanced Fair Task Schedul-ing for Cloud-Fog-Edge Systems." IEEE Transactions on Industri-al Informatics, 17(4), 2567-2576. DOI: 10.1109/TII.2020.3023184buyya.com
[13] Baniata, H., Anaqreh, A., Kertesz, A.: PF-BTS: a privacy-aware fog-enhanced blockchain-assisted task scheduling. Inf. Process. Manage. (2021). https:// doi. org/ 10. 1016/j. ipm. 2020. 102393.
[14] Mirmohseni, S.M.; Tang, C.; Javadpour, A. FPSO-GA: A Fuzzy Metaheuristic Load Balancing Algorithm to Reduce Energy Con-sumption in Cloud Networks. Wirel. Pers. Commun. 2022, 127, 2799–2821.
[15] Malik, M.; Suman. Lateral Wolf Based Particle Swarm Optimiza-tion (LW-PSO) for Load Balancing on Cloud Computing. Wirel. Pers. Commun. 2022, 1, 1–20.
[16] Baniata, H., Anaqreh, A., Kertesz, A.: PF-BTS: a privacy-aware fog-enhanced blockchain-assisted task scheduling. Inf. Process. Manage. (2021). https:// doi. org/ 10. 1016/j. ipm. 2020. 102393.
[17] Beraldi R, Canali C, Lancellotti R, Mattia G. Distributed load bal-ancing for heterogeneous fog computing infrastructures in smart cities. Pervasive Mob Comput 2020;67. https://doi.org/10.1016/j.pmcj.2020.101221.
[18] Sumathi M, Vijayaraj N, Raja SP, Rajkamal M. HHO-ACO hy-bridized load balancing technique in cloud computing. Int J Inf Technol 2023;15(3):1357–65.
[19] Gabhane JP, Pathak S, Thakare NM. A novel hybrid multi-resource load balancing approach using ant colony optimization with Tabu search for cloud computing. Innov Syst Softw Eng 2023;19(1):81–90. https://doi.org/10.1007/s11334-022-00508- 9.
[20] Salimi, R., Azizi, S., & Dogani, J. (2025). A hybrid priority-aware genetic algorithm and opposition-based learning for scheduling IoT tasks in green fog computing. Computer Networks, 267, Arti-cle 111349.
[21] Alsadie, D., & Alsulami, M. (2025). Modified grey wolf optimiza-tion for energy-efficient Internet of Things task scheduling in fog computing. Scientific Reports, 15, Article 14730.
[22] Rateb, R., Hadi, A. A., Tamanampudi, V. M., Abualigah, L., Ezugwu, A. E., et al. (2025). An optimal workflow scheduling in IoT–fog–cloud systems for minimizing time and energy. Scientific Reports, 15, Article 3607.
[23] Nazeri, M., Soltanaghaei, M., & Khorsand, R. (2024). A predictive energy-aware scheduling framework for scientific workflows in fog computing using MAPE-K and ANFIS. Expert Systems with Appli-cations, 247, Article 123192.
[24] S. Sohrabi, M. Sakhaei-nia, M. Nassiri, and R. Mohammadi, “Fog-based architecture and efficient task offloading methodology in IoT-based applications for smart irrigation system,” Computing, vol. 107, no. 3, p. 76, 2025.
[25] N. Jangu and Z. Raza, “Improved Jellyfish Algorithm-based multi-aspect task scheduling model for IoT tasks over fog integrated cloud environment,” Journal of Cloud Computing, vol. 11, no. 1, p. 98, 2022.
[26] Khaledian, Navid, et al. "An energy-efficient and deadline-aware workflow scheduling algorithm in the fog and cloud environment." Computing 106.1 (2024): 109-137.‏
[27] Liu, W., Li, C., Zheng, A., Zheng, Z., Zhang, Z., & Xiao, Y. (2023). Fog computing resource-scheduling strategy in IoT based on artifi-cial bee colony algorithm. Electronics, 12(7), 1511.‏
Journal of Soft Computing and Information Technology
Volume 15, Issue 2
Summer - August
Summer 2026
Pages 48-63