A Hybrid Cuckoo Search and Manta Ray Foraging Optimization Algorithm for Multi-Objective Task Scheduling in Cloud Computing

batool hamid

doi:10.71229/xpkazr17

Authors

Batool hamid Khalaf College of Engineering, Al-Nahrain University, Jadriya, Baghdad, Iraq

DOI:

https://doi.org/10.71229/xpkazr17

Keywords:

Metaheuristic optimization ; , Cloud computing; , Cuckoo Search; , Task scheduling;, Multi-objective optimization, ; Manta Ray Foraging Optimization; , Pareto front

Abstract

Task scheduling in cloud computing is an NP-hard multi-objective problem that requires the simultaneous optimization of makespan, execution cost, and energy consumption. Although individual metaheuristic algorithms can be strong optimizers, they often fail to achieve a reasonable exploration/exploitation balance across varying problem scales. This paper presents a hybrid algorithm, H-CS-MRFO, that combines the global exploration of Cuckoo Search (CS) through an adaptive Lévy flight mechanism with the local exploitation of Manta Ray Foraging Optimization (MRFO), which employs chain, cyclone, and somersault foraging strategies. A weighted-sum Pareto method simultaneously minimizes three conflicting objectives. Synthetic workload experiments (50–1,000 tasks, 13 heterogeneous VMs, 10 independent runs) indicate that H-CS-MRFO statistically outperforms three competitive baselines (MMWOA, MMRFO, MCS): makespan is reduced by up to 5.6%, execution cost is decreased by 1.4–2.1%, and power consumption is reduced by 4.5–6.8%, while a well-distributed Pareto front is obtained. Wilcoxon signed-rank tests confirm the statistical significance (p < 0.05) of each metric across each task scale.

References

[1] Sa’ad, S., Muhammed, A., Abdullahi, M., & Abdullah, A. (2023). An optimised cuckoo-based discrete symbiotic organisms search strategy for tasks scheduling in cloud computing environment. arXiv preprint arXiv:2311.15358.

[2] Ibrahim, I. M., et al. (2021). Task scheduling algorithms in cloud computing: A review. Turkish Journal of Computer and Mathematics Education, 12(4), 1041–1053.

[3] Murad, S. A., et al. (2022). A review on job scheduling technique in cloud computing and priority rule based intelligent framework. Journal of King Saud University – Computer and Information Sciences, 34(6), 2309–2331.

[4] Khan, M. S. A., & Santhosh, R. (2022). Task scheduling in cloud computing using hybrid optimization algorithm. Soft Computing, 26(23), 13069–13079.

[5] Nabi, S., Ahmad, M., Ibrahim, M., & Hamam, H. (2022). AdPSO: Adaptive PSO-based task scheduling approach for cloud computing. Sensors, 22(3), 1083.

[6] Zhu, Q.-H., et al. (2021). Task scheduling for multi-cloud computing subject to security and reliability constraints. IEEE/CAA Journal of Automatica Sinica, 8(4), 848–865.

[7] Hussain, M., et al. (2021). Energy and performance-efficient task scheduling in heterogeneous virtualized cloud computing. Sustainable Computing: Informatics and Systems, 30, 100517.

[8] Jaber, S., Ali, Y., & Ibrahim, N. (2022). An automated task scheduling model using a multi-objective improved cuckoo optimization algorithm. International Journal of Intelligent Engineering and Systems, 15(1), 295–304.

[9] Ghafari, R., Kabutarkhani, F. H., & Mansouri, N. (2022). Task scheduling algorithms for energy optimization in cloud environment: A comprehensive review. Cluster Computing.

[10] Yang, X. S., & Deb, S. (2009). Cuckoo search via Lévy flights. In Proc. World Congress on Nature and Biologically Inspired Computing (NaBIC) (pp. 210–214).

[11] Zhao, W., Zhang, Z., & Wang, L. (2020). Manta ray foraging optimization: A novel bio-inspired optimizer. Expert Systems with Applications, 159, 113562.

[12] Braun, T. D., et al. (2001). A comparison of eleven static heuristics for mapping a class of independent tasks onto heterogeneous distributed computing systems. Journal of Parallel and Distributed Computing, 61(6), 810–837.

[13] Weiqing, W., & Yanru, W. (2021). A load-balanced genetic algorithm for task scheduling in cloud computing. Cluster Computing, 24, 2209–2224.

[14] Yin, X., Xu, Z., & Zhao, J. (2018). Dual fitness genetic algorithm for task scheduling in cloud computing. Future Generation Computer Systems, 79, 1–9.

[15] Pirozmand, P., Rahmani, A., & Yazdani, A. (2023). An improved particle swarm optimization algorithm for task scheduling in cloud computing. Journal of Ambient Intelligence and Humanized Computing, 14, 4313–4327.

[16] Valarmathi, S., & Sheela, K. (2019). Real-time task scheduling using hybrid PSO-bat algorithm in cloud computing. Cluster Computing, 22(1), 2141–2151.

[17] Nanjappan, R., Prakash, P., & Praveen, M. (2023). Task scheduling in cloud computing using improved Grey Wolf Optimizer. Computers and Electrical Engineering, 109, 108781.

[18] Wei, L. (2020). Ant colony optimization for efficient task scheduling in cloud computing. Applied Soft Computing, 93, 106365.

[19] Zhang, Z., Liu, H., & Li, Y. (2024). Multi-objective honey badger algorithm for cloud task scheduling. Applied Soft Computing, 155, 111210.

[20] Cui, X., Zhao, L., & Chen, Y. (2023). Multi-objective task scheduling in cloud computing using evolutionary multi-factorial optimization. Future Generation Computer Systems, 150, 66–81.

[21] Zhu, Y., Liu, H., & Wang, J. (2022). Task scheduling in cloud computing using NSGA-III. IEEE Access, 10, 32502–32518.

[22] Jin, H., Wang, J., & Liu, F. (2023). Preference-based multi-objective optimization for cloud task scheduling. IEEE Transactions on Cloud Computing (early access).

[23] Abdel-Basset, M., Elhoseny, M., & El-Khouly, K. S. M. (2022). Enhanced cuckoo search algorithm for efficient cloud computing task scheduling. Mathematics, 10(21), 4049.

[24] Zhan, J., Li, X., Zhang, L., & Sun, Y. (2020). A survey of metaheuristic algorithms for cloud computing task scheduling. IEEE Access, 8, 129379–129395.

[25] Issa, A. S., Jaber, S. S., Ali, Y. H., & Rashid, T. A. (2025). A task scheduling algorithm for cloud computing based on bio-inspired multi-objective manta rays. In Multi-objective Optimization Techniques (pp. 81–98). CRC Press.

[26] Abualigah, L., & Diabat, A. (2021). A novel hybrid ant lion optimization algorithm for task scheduling in cloud computing. Soft Computing, 25, 13465–13482.

[27] Aslahi-Shahri, H., Pourpanah, H. R., & Hussain, S. A. (2016). Hybrid cuckoo search and genetic algorithm for feature selection. Neural Computing and Applications, 27(5), 1387–1396.

[28] Parthasaradi, R., Rajagopalan, S., & Manikandan, N. (2024). Hybrid Horse Herd–Squirrel Search Algorithm for multi-objective task scheduling in cloud computing. Expert Systems with Applications, 244, 123002.