Advancing cloud load balancing : An energy-aware model using a hybrid genetic and nature-inspired algorithm
*Yashika SharmaCorresponding authoryashika.sharma85@gmail.comDepartment of Computer Science and TechnologySchool of EngineeringManav Rachna UniversityFaridabad, Haryana, 121003, India0000-0002-6367-5852View full profile → , Sachin Lakrasachin@mru.edu.inDepartment of Computer Science and TechnologySchool of EngineeringManav Rachna UniversityFaridabad, Haryana, 121003, India0000-0002-6977-4974View full profile →
* Corresponding author · click or hover a name for details
- Received:
- 01 Mar 2025
- Published Online:
- 12 Mar 2026
- Article type:
- Research Article
- Language:
- EN
- Article no.:
- JIOS-2113
- Pages:
- 1145–1162
Abstract
Keywords
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References
[1] S. K. Panda and P. K. Jana, “An energy-efficient task scheduling algorithm for heterogeneous cloud computing systems,” Cluster Comput., vol. 22, no. 2, pp. 509–527 (2019).
[2] D. Jiang, Y. Wang, Z. Lv, W. Wang, and H. Wang, “An energy-efficient networking approach in cloud services for IIoT networks,” IEEE J. Sel. Areas Commun., vol. 38, no. 5, pp. 928–941 (2020).
[3] A. Ali, M. M. Iqbal, H. Jamil, F. Qayyum, S. Jabbar, O. Cheikhrouhou, and F. Jamil, “An efficient dynamic-decision-based task scheduler for task offloading optimization and energy management in mobile cloud computing,” Sensors, vol. 21, no. 13, p. 4527 (2021).
[4] M. Ghobaei-Arani, “A workload clustering-based resource provisioning mechanism using biogeography-based optimization technique in cloud-based systems,” Soft Comput., vol. 25, no. 5, pp. 3813–3830 (2021).
[5] Z. Ali, L. Jiao, T. Baker, G. Abbas, Z. H. Abbas, and S. Khaf, “A deep learning approach for energy-efficient computational offloading in mobile edge computing,” IEEE Access, vol. 7, pp. 149623–149633 (2019).
[6] R. Garg, M. Mittal, and L. H. Son, “Reliability- and energy-efficient workflow scheduling in cloud environment,” Cluster Comput., vol. 22, no. 4, pp. 1283–1297 (2019).
[7] D. Ding, X. Fan, Y. Zhao, K. Kang, Q. Yin, and J. Zeng, “Q-learning-based dynamic task scheduling for energy-efficient cloud computing,” Future Gener. Comput. Syst., vol. 108, pp. 361–371 (2020).
[8] Y. Sharma and S. Lakra, “Green cloud job scheduling and load balancing using hybrid biogeography-based optimization and genetic algorithm,” in Micro-Electronics and Telecommunication Engineering, Proc. 3rd ICMETE, Singapore: Springer, pp. 185–195 (2019).
[9] R. Medara and R. S. Singh, “Energy-efficient and reliability-aware workflow task scheduling in cloud environment,” Wireless Pers. Commun., vol. 119, no. 2, pp. 1301–1320 (2021).
[10] J. Praveenchandar and A. Tamilarasi, “Dynamic resource allocation with optimized task scheduling and improved power management in cloud computing,” J. Ambient Intell. Humaniz. Comput., vol. 12, pp. 4147–4159 (2021).
[11] A. Marahatta, S. Pirbhulal, F. Zhang, R. M. Parizi, K.-K. R. Choo, and Z. Liu, “Classification-based and energy-efficient dynamic task scheduling scheme for virtualized cloud data centers,” IEEE Trans. Cloud Comput., vol. 9, no. 4, pp. 1376–1390 (2019).
[12] Y. Sharma and S. Lakra, “A comparative study of cloud job scheduling algorithms with a hybrid genetic and biogeography-based optimization algorithm,” communicated (2025).
[13] H. A. Younis, I. M. Hayder, I. S. Seger, and H. A. K. Younis, “Design and implementation of a system that preserves the confidentiality of stream cipher in non-linear flow coding,” J. Discrete Math. Sci. Cryptogr., vol. 23, no. 4, pp. 1409–1419 (2020), doi: 10.1080/09720529.2020.1714890.
[14] I. Cherkaoui and F. Zinoun, “On the use of Egyptian fractions for stream ciphers,” J. Discrete Math. Sci. Cryptogr., vol. 26, no. 1, pp. 139–152 (2023), doi: 10.1080/09720529.2021.1923921.
[15] Y. L. Liu and F. Gomide, “A participatory search algorithm,” Evol. Intell., vol. 10, no. 1, pp. 23–43 (2017).
[16] OpenCloud, “Hadoop cluster trace: Format and schema.” [Online]. Available: https://ftp.pdl.cmu.edu/pub/datasets/hla/dataset.html




