Cloud Task Scheduling Techniques: A Survey of Greedy, Machine Learning, and Metaheuristic Approaches
DOI:
https://doi.org/10.63282/3050-922X.IJERET-V6I3P111Keywords:
Cloud Computing, Task Scheduling Algorithms, Greedy Algorithms, Machine Learning, QOS, Computational Efficiency, Adaptive Scheduling, Reinforcement LearningAbstract
Cloud computing has emerged as an enabling technology, that offers scalability, elastic, on-demand computing and storage resources through internet servers. Appropriate scheduling of tasks in cloud systems is a requisite in achieving an optimal performance scale, minimizing costs of operation and achieving Quality of Service (QoS) demands. This paper is a review of the existing task scheduling methods with a detailed description of the most popular of them related to greedy algorithms, machine learning (ML), and metaheuristic approaches. Greedy algorithms including First Come First Serve (FCFS), Shortest Job First (SJF) are simple and computation efficient, yet they are not usually adaptable and, might not be globally optimized. Schedulers based on machine learning have been developed to use historical information, adaptive models to better assist decision-making under dynamic conditions, and use supervised, reinforcement and online learning techniques. Natural-yet-inspired metaheuristic algorithms, (e.g. Genetic Algorithms (GA), Particle Swarm Optimization (PSO), Ant Colony Optimization (ACO)) offer strong global search properties well-suited to complex multi-objective optimization. Comparative analyses suggest that metaheuristic and machine learning approaches inheritably deliver better productivity in terms of makespan, resource consumption, and energy efficiency than conventional greedy based methods. The combination of these methods holds great potential in terms of cloud systems concerning heterogeneity, scalability, and dynamic workload handling issues. The survey exclaims its significance of adaptive, hybrid scheduling structures to address changing needs within the large-scale cloud infrastructures
References
[1] S. P. Bheri and G. Modalavalasa, “Advancements in Cloud Computing for Scalable Web Development: Security Challenges and Performance Optimization,” J. Comput. Technol. Int. J., vol. 13, no. 12, 2024.
[2] S. Shilpashree, R. R. Patil, and C. Parvathi, “‘Cloud computing an overview,’” Int. J. Eng. Technol., 2018, doi: 10.14419/ijet.v7i4.10904.
[3] C. Shyalika, T. Silva, and A. Karunananda, “Reinforcement Learning in Dynamic Task Scheduling: A Review,” SN Comput. Sci., vol. 1, no. 6, Nov. 2020, doi: 10.1007/s42979-020-00326-5.
[4] G. Maddali and S. J. Wawge, Site Reliability Engineering. 2025.
[5] A. Jain, M. Saini, and M. Kumar, “Greedy Algorithm,” J. Adv. Res. Comput. Sci. Eng. (ISSN 2456-3552), 2015, doi: 10.53555/nncse.v2i4.451.
[6] A. Chadha, S. Sharma, and V. Arora, Computational Science and Its Applications. 2023. doi: 10.1201/9781003347484.
[7] B. K. R. Janumpally, “Intelligent Energy Aware Efficient Task Scheduling in Cloud Computing: Leveraging Swarm Optimization Algorithms for Improve Resource Utilization,” in 2025 1st International Conference on Radio Frequency Communication and Networks (RFCoN), IEEE, Jun. 2025, pp. 1–6. doi: 10.1109/RFCoN62306.2025.11085278.
[8] S. S. S. Neeli, “Heart Disease Prediction For A Cloud-Based Smart Healthcare Monitoring System Using Gans And Ant Colony Optimization,” Int. J. Med. Public Heal., vol. 14, no. 4, p. 11, 2024.
[9] P. Agrawal, H. F. Abutarboush, T. Ganesh, and A. W. Mohamed, “Metaheuristic algorithms on feature selection: A survey of one decade of research (2009-2019),” IEEE Access, 2021, doi: 10.1109/ACCESS.2021.3056407.
[10] D. D. Rao, S. Madasu, S. R. Gunturu, C. D’britto, and J. Lopes, “Cybersecurity Threat Detection Using Machine Learning in Cloud-Based Environments: A Comprehensive Study,” Int. J. Recent Innov. Trends Comput. Commun., vol. 12, no. 1, 2024.
[11] G. Maddali, “An Efficient Bio-Inspired Optimization Framework for Scalable Task Scheduling in Cloud Computing Environments,” Int. J. Curr. Eng. Technol., vol. 15, no. 3, 2025.
[12] M. Abdel-Basset, R. Mohamed, W. Abd Elkhalik, M. Sharawi, and K. M. Sallam, “Task Scheduling Approach in Cloud Computing Environment Using Hybrid Differential Evolution,” Mathematics, vol. 10, no. 21, p. 4049, Oct. 2022, doi: 10.3390/math10214049.
[13] C. Carrión, “Kubernetes Scheduling: Taxonomy, Ongoing Issues and Challenges,” ACM Comput. Surv., vol. 55, no. 7, pp. 1–37, Jul. 2023, doi: 10.1145/3539606.
[14] T. Rausch, A. Rashed, and S. Dustdar, “Optimized container scheduling for data-intensive serverless edge computing,” Futur. Gener. Comput. Syst., 2021, doi: 10.1016/j.future.2020.07.017.
[15] A. R. Duggasani, “Scalable and Optimized Load Balancing in Cloud Systems: Intelligent Nature-Inspired Evolutionary Approach,” Int. J. Innov. Sci. Res. Technol., vol. 10, no. 5, May 2025, doi: 10.38124/ijisrt/25may1290.
[16] C. F. Kurz, W. Maier, and C. Rink, “A greedy stacking algorithm for model ensembling and domain weighting,” BMC Res. Notes, 2020, doi: 10.1186/s13104-020-4931-7.
[17] H. Lee, S. H. Moon, J. Y. Hong, J. Lee, and S. H. Hyun, “A Machine Learning Approach Using FDG PET-Based Radiomics for Prediction of Tumor Mutational Burden and Prognosis in Stage IV Colorectal Cancer,” Cancers (Basel)., 2023, doi: 10.3390/cancers15153841.
[18] A. I. Kadhim, “Survey on supervised machine learning techniques for automatic text classification,” Artif. Intell. Rev., vol. 52, no. 1, pp. 273–292, Jun. 2019, doi: 10.1007/s10462-018-09677-1.
[19] B. R. Kiran et al., “Deep Reinforcement Learning for Autonomous Driving: A Survey,” IEEE Trans. Intell. Transp. Syst., vol. 23, no. 6, pp. 4909–4926, Jun. 2022, doi: 10.1109/TITS.2021.3054625.
[20] J. Bassen et al., “Reinforcement Learning for the Adaptive Scheduling of Educational Activities,” in Conference on Human Factors in Computing Systems - Proceedings, 2020. doi: 10.1145/3313831.3376518.
[21] A. Peiris, F. K. P. Hui, C. Duffield, and T. Ngo, “Production scheduling in modular construction: Metaheuristics and future directions,” Autom. Constr., vol. 150, p. 104851, Jun. 2023, doi: 10.1016/j.autcon.2023.104851.
[22] A. A. Aloudah and O. Banimelhem, “An Enhanced Task Scheduling Algorithm for Cloud Computing Environments,” in 2025 16th International Conference on Information and Communication Systems (ICICS), IEEE, Jul. 2025, pp. 1–5. doi: 10.1109/ICICS65354.2025.11073117.
[23] M. N. H. Roudsari, “Improved task scheduling in heterogeneous distributed systems using intelligent greedy harris hawk optimization algorithm,” Evol. Intell., vol. 17, no. 5–6, pp. 4199–4226, Oct. 2024, doi: 10.1007/s12065-024-00979-8.
[24] S. Lipsa, R. K. Dash, N. Ivković, and K. Cengiz, “Task Scheduling in Cloud Computing: A Priority-Based Heuristic Approach,” IEEE Access, vol. 11, pp. 27111–27126, 2023, doi: 10.1109/ACCESS.2023.3255781.
[25] S. M. Almufti, A. A. Shaban, R. I. Ali, and J. A. Dela Fuente, “Overview of Metaheuristic Algorithms,” Polaris Glob. J. Sch. Res. Trends, vol. 2, no. 2, pp. 10–32, Apr. 2023, doi: 10.58429/pgjsrt.v2n2a144.
[26] D. Mukherjee, S. Ghosh, S. Pal, A. A. Aly, and D.-N. Le, “Adaptive Scheduling Algorithm Based Task Loading in Cloud Data Centers,” IEEE Access, vol. 10, pp. 49412–49421, 2022, doi: 10.1109/ACCESS.2022.3168288.
[27] N. Gupta, M. Batra, and A. Khosla, “Optimizing Greedy Algorithm to Balance the Server Load in Cloud Simulated Environment,” in Proceedings of the 3rd International Conference on Inventive Research in Computing Applications, ICIRCA 2021, 2021. doi: 10.1109/ICIRCA51532.2021.9544107.
[28] M. Karaja, M. Ennigrou, and L. Ben Said, “Budget-constrained dynamic Bag-of-Tasks scheduling algorithm for heterogeneous multi-cloud environment,” in 2020 International Multi-Conference on: “Organization of Knowledge and Advanced Technologies” (OCTA), IEEE, Feb. 2020, pp. 1–6. doi: 10.1109/OCTA49274.2020.9151737.
