Advance Hybrid Model in Cloud Computing for Task Scheduling and Resources Allocation Using Meta Heuristic Machine Learning Model
Manikandan Nanjappan
Department of Data Science and Business Systems, SRM Institute of Science and Technology, Chennai, Tamil Nadu, India and Department of Electronic Engineering, National Kaohsiung University of Science and Technology, Kaohsiung, Taiwan.
Modern technology requires cloud computing. Allocating resources and scheduling tasks are crucial components of cloud computing. Nondeterministic polynomial completeness (NP) of cloud systems makes job scheduling one of the most challenging aspects of cloud communications. This research proposes novel technique in advancements in hybrid model for task scheduling and resource allocation using meta-heuristic machine learning model in cloud computing networks. Here the cloud network is deployed with numbers of users and clients with virtual machines. The task scheduling model for this deployed network is carried out using convolutional transfer graph proximal policy-based firefly harmony search cat optimization. Then the resource allocation is carried out using software defined virtual machine-based reinforcement markov model. the experimental analysis is carried out in terms of resource utilization, network efficiency, throughput, latency, QoS. For a particular collection of jobs, our primary contribution is to decrease processing time as well as boost speed and efficiency. The proposed technique attained resource utilization of 45%, network efficiency of 96%, throughput of 97%, latency of 95%, QoS of 98%.
S. Gurusamy and R. Selvaraj, “Resource allocation with efficient task scheduling in cloud computing using hierarchical auto-associative polynomial convolutional neural network,” Expert Systems with Applications, vol. 249, p. 123554, Sep. 2024, doi: 10.1016/j.eswa.2024.123554.
V. R. S. P. Alla et al., “Optimizing task scheduling in cloud computing: a hybrid artificial intelligence approach,” Cogent Engineering, vol. 11, no. 1, Mar. 2024, doi: 10.1080/23311916.2024.2328355.
S. Mangalampalli, G. R. Karri, M. Kumar, O. I. Khalaf, C. A. T. Romero, and G. A. Sahib, “DRLBTSA: Deep reinforcement learning based task-scheduling algorithm in cloud computing,” Multimedia Tools and Applications, vol. 83, no. 3, pp. 8359–8387, Jun. 2023, doi: 10.1007/s11042-023-16008-2.
D. Komarasamy, S. M. Ramaganthan, D. M. Kandaswamy, and G. Mony, “Deep learning and optimization enabled multi-objective for task scheduling in cloud computing,” Network: Computation in Neural Systems, vol. 36, no. 1, pp. 79–108, Aug. 2024, doi: 10.1080/0954898x.2024.2391395.
S. Kaur, J. Singh, and V. Bharti, “A Comparative Study of Optimization Based Task Scheduling in Cloud Computing Environments Using Machine Learning,” 2024 5th International Conference on Intelligent Communication Technologies and Virtual Mobile Networks (ICICV), pp. 731–740, Mar. 2024, doi: 10.1109/icicv62344.2024.00122.
A. S. Rajawat, S. B. Goyal, M. Kumar, and V. Malik, “Adaptive resource allocation and optimization in cloud environments: Leveraging machine learning for efficient computing,” Applied Data Science and Smart Systems, pp. 499–508, Jun. 2024, doi:10.1201/978100347105964.
M. W. Yue Dong, “Projecting the Development of Accelerator Technologies Using Growth Models and Social Cost Benefit Frameworks,” Journal of Digital Business and International Marketing, pp. 109–118, Apr. 2025, doi: 10.64026/jdbim/2025012.
N. Devi, S. Dalal, K. Solanki, S. Dalal, U. K. Lilhore, S. Simaiya, & N. Nuristani, “A systematic literature review for load balancing and task scheduling techniques in cloud computing,” Artificial Intelligence Review, 57(10), 276, (2024).
X. Wang, Y. Laili, L. Zhang, and Y. Liu, “Hybrid Task Scheduling in Cloud Manufacturing with Sparse-Reward Deep Reinforcement Learning,” IEEE Transactions on Automation Science and Engineering, vol. 22, pp. 1878–1892, 2025, doi: 10.1109/tase.2024.3371250.
S. Sharma, & P. S. Rawat, “Efficient resource allocation in cloud environment using SHO-ANN-based hybrid approach. Sustainable Operations and Computers, 5, 141-155, (2024).
M. D’Souza, C. Kaur, A. S. Bisht, D. Nimma, G. Dhanalakshmi, and M. K. M. Faizal, “Hybrid Deep Learning Framework for Dynamic and Energy-Efficient Workload Migration in Cloud Computing Environments,” 2024 International Conference on Communication, Control, and Intelligent Systems (CCIS), pp. 1–6, Dec. 2024, doi: 10.1109/ccis63231.2024.10932009.
H. Zavieh, A. Javadpour, & A. K. Sangaiah, “Efficient task scheduling in cloud networks using ANN for green computing,” International Journal of Communication Systems, 37(5), e5689, (2024).
A. N. Malti, M. Hakem, and B. Benmammar, “A new hybrid multi-objective optimization algorithm for task scheduling in cloud systems,” Cluster Computing, vol. 27, no. 3, pp. 2525–2548, Jul. 2023, doi: 10.1007/s10586-023-04099-3.
M. Mahdizadeh, A. Montazerolghaem, and K. Jamshidi, “Task scheduling and load balancing in SDN-based cloud computing: A review of relevant research,” Journal of Engineering Research, Nov. 2024, doi: 10.1016/j.jer.2024.11.002.
S. S. Sefati, A. M. Nor, B. Arasteh, R. Craciunescu, and C.-R. Comsa, “A Probabilistic Approach to Load Balancing in Multi-Cloud Environments via Machine Learning and Optimization Algorithms,” Journal of Grid Computing, vol. 23, no. 2, Apr. 2025, doi: 10.1007/s10723-025-09805-6.
M. Afzali, A. Mohammad Vali Samani, & H. R. Naji, “An efficient resource allocation of IoT requests in hybrid fog–cloud environment,” The Journal of Supercomputing, 80(4), 4600-4624, (2024).
R. Sandhu, M. Faiz, H. Kaur, A. Srivastava, and V. Narayan, “Enhancement in performance of cloud computing task scheduling using optimization strategies,” Cluster Computing, vol. 27, no. 5, pp. 6265–6288, Feb. 2024, doi: 10.1007/s10586-023-04254-w.
P. Amini, & A. Kalbasi, “An adaptive task scheduling approach for cloud computing using deep reinforcement learning,” In 2024 Third International Conference on Distributed Computing and High-Performance Computing (DCHPC), (pp. 1-9). IEEE, 2024, May.
F. S. Alsubaei, A. Y. Hamed, M. R. Hassan, M. Mohery, and M. Kh. Elnahary, “Machine learning approach to optimal task scheduling in cloud communication,” Alexandria Engineering Journal, vol. 89, pp. 1–30, Feb. 2024, doi: 10.1016/j.aej.2024.01.040.
N. K, “Performance Evaluation of Fog Computing for Latency and Energy Efficiency in IoT Applications,” Journal of Computer and Communication Networks, pp. 22–32, Feb. 2025, doi: 10.64026/jccn/2025003.
S. Mangalampalli et al., “Multi Objective Prioritized Workflow Scheduling Using Deep Reinforcement Based Learning in Cloud Computing,” IEEE Access, vol. 12, pp. 5373–5392, 2024, doi: 10.1109/access.2024.3350741.
J. Jeon, S. Park, B. Jeong, and Y.-S. Jeong, “Efficient Container Scheduling with Hybrid Deep Learning Model for Improved Service Reliability in Cloud Computing,” IEEE Access, vol. 12, pp. 65166–65177, 2024, doi: 10.1109/access.2024.3396652.
Y. Pachipala, D. B. Dasari, V. V. R. M. Rao, P. Bethapudi, and T. Srinivasarao, “Workload prioritization and optimal task scheduling in cloud: introduction to hybrid optimization algorithm,” Wireless Networks, vol. 31, no. 1, pp. 945–964, Jul. 2024, doi: 10.1007/s11276-024-037933.
CRediT Author Statement
The authors confirm contribution to the paper as follows:
Conceptualization: Manikandan Nanjappan, Chin-Shiuh Shieh and Mong-Fong Horng;
Methodology: Manikandan Nanjappan;
Writing- Original Draft Preparation: Chin-Shiuh Shieh and Mong-Fong Horng;
Visualization: Manikandan Nanjappan;
Investigation: Manikandan Nanjappan, Chin-Shiuh Shieh and Mong-Fong Horng;
Supervision: Chin-Shiuh Shieh and Mong-Fong Horng;
Validation: Manikandan Nanjappan;
Writing- Reviewing and Editing: Manikandan Nanjappan, Chin-Shiuh Shieh and Mong-Fong Horng; All authors reviewed the results and approved the final version of the manuscript.
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Manikandan Nanjappan
Department of Data Science and Business Systems, SRM Institute of Science and Technology, Chennai, Tamil Nadu, India and Department of Electronic Engineering, National Kaohsiung University of Science and Technology, Kaohsiung, Taiwan.
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Cite this article
Manikandan Nanjappan, Chin-Shiuh Shieh and Mong-Fong Horng, “Advance Hybrid Model in Cloud Computing for Task Scheduling and Resources Allocation Using Meta Heuristic Machine Learning Model”, Journal of Machine and Computing, vol.5, no.3, pp. 1615-1627, July 2025, doi: 10.53759/7669/jmc202505128.
