Energy–Cost–SLA Aware Cloud Scheduling via Adaptive Non-dominated Sorting Genetic Algorithm-III and Neighborhood Refinement
Vijayasekaran G. , Sathya V., Sumathi S., Lavanya M.
Open Access
Volume - 8 • Issue - 1 • march 2026
Abstract
The optimization of resource allocation in a cloud computing environment is a problem that has been challenging due to heterogeneous tasks with varying resource requirements and different optimization objectives for execution time, energy consumption, service level agreements (SLAs) and so on. In this paper, a hybrid multi-objective optimization algorithm called AH-NSGAIII-VND is proposed for solving a multi-objective optimization problem in a cloud computing environment. The proposed algorithm integrates a global search process using a variant of a multi-objective evolutionary algorithm called Non-Dominated Sorting Genetic Algorithm III (NSGA-III) and a local search process using a variant of a local search algorithm called Variable Neighborhood Descent (VND). The problem of resource allocation in a cloud computing environment is formulated as a multi-objective optimization problem considering makespan, energy consumption, cost, service level agreements (SLAs) and resource utilization. Researchers compared the results of the PSO, GA, and GWO optimization methods to the baseline using CloudSim-based model conditions on the changing workload scale. Experimental results indicate that cloud supply governance efficiency is enhanced with the AH-NSGAIII-VND architecture. It achieves around 11.3%, 12.8%, and 28% lower costs than the baseline NSGA-III approach while increasing overall asset utilization by over 5 percentage points. Moreover, with increasing workload, the proposed model exhibits improved convergence behavior and scalability. These results confirm that global evolutionary optimization supported by adaptive local search successfully reinstates or enhances the efficiency of resource allocation.
Cite this article
G., Vijayasekaran, Sathya V., Sumathi S., and Lavanya M.. "Energy–Cost–SLA Aware Cloud Scheduling via Adaptive Non-dominated Sorting Genetic Algorithm-III and Neighborhood Refinement." Journal of Trends in Computer Science and Smart Technology 8, no. 1 (2026): 63-88. doi: 10.36548/jtcsst.2026.1.004
Copy Citation
G., V., V., S., S., S., & M., L. (2026). Energy–Cost–SLA Aware Cloud Scheduling via Adaptive Non-dominated Sorting Genetic Algorithm-III and Neighborhood Refinement. Journal of Trends in Computer Science and Smart Technology, 8(1), 63-88. https://doi.org/10.36548/jtcsst.2026.1.004
Copy Citation
G., Vijayasekaran, et al. "Energy–Cost–SLA Aware Cloud Scheduling via Adaptive Non-dominated Sorting Genetic Algorithm-III and Neighborhood Refinement." Journal of Trends in Computer Science and Smart Technology, vol. 8, no. 1, 2026, pp. 63-88. DOI: 10.36548/jtcsst.2026.1.004.
Copy Citation
G. V, V. S, S. S, M. L. Energy–Cost–SLA Aware Cloud Scheduling via Adaptive Non-dominated Sorting Genetic Algorithm-III and Neighborhood Refinement. Journal of Trends in Computer Science and Smart Technology. 2026;8(1):63-88. doi: 10.36548/jtcsst.2026.1.004
Copy Citation
V. G., S. V., S. S., and L. M., "Energy–Cost–SLA Aware Cloud Scheduling via Adaptive Non-dominated Sorting Genetic Algorithm-III and Neighborhood Refinement," Journal of Trends in Computer Science and Smart Technology, vol. 8, no. 1, pp. 63-88, Mar. 2026, doi: 10.36548/jtcsst.2026.1.004.
Copy Citation
G., V., V., S., S., S. and M., L. (2026) 'Energy–Cost–SLA Aware Cloud Scheduling via Adaptive Non-dominated Sorting Genetic Algorithm-III and Neighborhood Refinement', Journal of Trends in Computer Science and Smart Technology, vol. 8, no. 1, pp. 63-88. Available at: https://doi.org/10.36548/jtcsst.2026.1.004.
Copy Citation
@article{g.2026,
author = {Vijayasekaran G. and Sathya V. and Sumathi S. and Lavanya M.},
title = {{Energy–Cost–SLA Aware Cloud Scheduling via Adaptive Non-dominated Sorting Genetic Algorithm-III and Neighborhood Refinement}},
journal = {Journal of Trends in Computer Science and Smart Technology},
volume = {8},
number = {1},
pages = {63-88},
year = {2026},
publisher = {IRO Journals},
doi = {10.36548/jtcsst.2026.1.004},
url = {https://doi.org/10.36548/jtcsst.2026.1.004}
}
Copy Citation
Keywords
Cloud Computing
Resource Allocation
Multi-Objective Optimization
Hybrid Optimization
NSGA-III
SLA Management
References
- Kaur, Gurleen, and Anju Bala. "A Survey of Prediction-Based Resource Scheduling Techniques for Physics-Based Scientific Applications." Modern Physics Letters B 32, no. 25 (2018): 1850295.
- Miuccio, Luciano, Daniela Panno, Pietro Pisacane, and Salvatore Riolo. "A QoS-Aware and Channel-Aware Radio Resource Management Framework for Multi-Numerology Systems." Computer Communications 191 (2022): 299-314.
- Kayalvili, S., R. Senthilkumar, S. Yasotha, and R. S. Kamalakannan. "An Optimized Resource Allocation in Cloud Using Prediction Enabled Reinforcement Learning." Scientific Reports 15, no. 1 (2025): 36088.
- Amini Motlagh, Aida, Ali Movaghar, and Amir Masoud Rahmani. "A New Reliability‐Based Task Scheduling Algorithm in Cloud Computing." International Journal of Communication Systems 35, no. 3 (2022): e5022.
- Pan, Jiahui, Yi Wei, Lei Meng, and Xiangxu Meng. "A Dual Scheduling Framework for Task and Resource Allocation in Clouds Using Deep Reinforcement Learning." Journal of King Saud University Computer and Information Sciences 37, no. 5 (2025): 81.
- Cui, Tongke, Ruopeng Yang, Chao Fang, and Shui Yu. "Deep Reinforcement Learning-Based Resource Allocation for Content Distribution in IoT-Edge-Cloud Computing Environments." Symmetry 15, no. 1 (2023): 217.
- Zhang, Jixian, Ning Xie, Xuejie Zhang, Kun Yue, and Weidong Li. "Machine Learning Based Resource Allocation of Cloud Computing in Auction." Computers, Materials & Continua 56, no. 1 (2018).
- Dey, Niladri, T. Gunasekhar, and K. Purnachand. "ACO-Inspired Load Balancing Strategy for Cloud-Based Data Centre with Predictive Machine Learning Approach." Computers, Materials, & Continua 75, no. 1 (2023): 513.
- Agarwal, Mohit, and Shikha Gupta. "An Adaptive Genetic Algorithm-Based Load Balancing-Aware Task Scheduling Technique for Cloud Computing." Computers, Materials, & Continua 73, no. 3 (2022): 6103.
- Hamed, Ahmed Y., M. Kh Elnahary, Faisal S. Alsubaei, and Hamdy H. El-Sayed. "Optimization Task Scheduling Using Cooperation Search Algorithm for Heterogeneous Cloud Computing Systems." Computers, Materials & Continua 74, no. 1 (2023).
- Dong, Junyu, Songtao Gao, Haijing Lu, Yangyang Cao, Yiming Yu, and Xiangchen Ma. "Joint Optimization of Resource Allocation and Tasks Scheduling in Network Slicing Enabled Internet of Vehicles." In 2022 IEEE 8th International Conference on Computer and Communications (ICCC), IEEE, 2022, 552-556.
- Qiao, Kai, Hongchao Wang, Weiting Zhang, Dong Yang, Yuming Zhang, and Ning Zhang. "Resource Allocation for Network Slicing in Open RAN: A Hierarchical Learning Approach." IEEE Transactions on Cognitive Communications and Networking 11, no. 4 (2025): 2584-2600.
- Chen, Jing, Tiantian Du, and Gongyi Xiao. "A Multi-Objective Optimization for Resource Allocation of Emergent Demands in Cloud Computing." Journal of Cloud Computing 10, no. 1 (2021): 20.
- Laili, Yuanjun, Sisi Lin, and Diyin Tang. "Multi-Phase Integrated Scheduling of Hybrid Tasks in Cloud Manufacturing Environment." Robotics and Computer-Integrated Manufacturing 61 (2020): 101850.
- Abbasi, Mahdi, Ehsan Mohammadi Pasand, and Mohammad R. Khosravi. "Workload Allocation in IoT-Fog-Cloud Architecture Using a Multi-Objective Genetic Algorithm." Journal of Grid Computing 18, no. 1 (2020): 43-56.
- Nguyen, Duong Tuan, Chuan Pham, Kim Khoa Nguyen, and Mohamed Cheriet. "Placement and chaining for run-time IoT service deployment in edge-cloud." IEEE Transactions on Network and Service Management 17, no. 1 (2019): 459-472.
- Yu, Shuai, Xu Chen, Zhi Zhou, Xiaowen Gong, and Di Wu. "When Deep Reinforcement Learning Meets Federated Learning: Intelligent Multitimescale Resource Management for Multiaccess Edge Computing in 5G Ultradense Network." IEEE Internet of Things Journal 8, no. 4 (2020): 2238-2251.
- Peng, Xiting, Kaoru Ota, and Mianxiong Dong. "Multiattribute-Based Double Auction Toward Resource Allocation in Vehicular Fog Computing." IEEE Internet of Things Journal 7, no. 4 (2020): 3094-3103.
- Xu, Xiaolong, Xihua Liu, Zhanyang Xu, Fei Dai, Xuyun Zhang, and Lianyong Qi. "Trust-Oriented IoT Service Placement for Smart Cities in Edge Computing." IEEE Internet of Things Journal 7, no. 5 (2019): 4084-4091.
- Tang, Xiaoan, Tianxiang Tang, Zibo Shen, Handong Zheng, and Weiping Ding. "Double Deep Q-Network-Based Dynamic Offloading Decision-Making for Mobile Edge Computing with Regular Hexagonal Deployment Structure of Servers." Applied Soft Computing 169 (2025): 112594.
- Badr, Shaimaa, Ahmed El Mahalawy, Gamal Attiya, and Aida A. Nasr. "Task consolidation Based Power Consumption Minimization in Cloud Computing Environment." Multimedia Tools and Applications 82, no. 14 (2023): 21385-21413.
- Zhang, Yibin, Jinlong Sun, Guan Gui, Haris Gacanin, and Hikmet Sari. "A Generalized Channel Dataset Generator for 5G New Radio Systems Based on Ray-Tracing." IEEE Wireless Communications Letters 10, no. 11 (2021): 2402-2406.
