Document Type : Original Article
Author
Department of Preparatory Year–Basic Sceinces, Umm Al-Qura University, Makkah, Saudi Arabia.
Abstract
Cloud computing is an essential tool for sharing resources across virtual machines, and it relies on scheduling and load balancing to ensure that tasks are assigned to the most appropriate resources. Multiple independent tasks need to be handled by cloud computing, and static and dynamic scheduling plays a crucial role in allocating tasks to the right resources. This is especially important in heterogeneous environments, where algorithms can improve load balancing and enhance cloud computing's efficiency. This paper aims to evaluate and discuss algorithms that can improve load balancing in cloud systems.
Keywords
[1] Bal, P. K., Mohapatra, S. K., Das, T. K., Srinivasan, K., & Hu, Y. C. (2022). A joint resource allocation, security with efficient task scheduling in cloud computing using hybrid machine learning techniques. Sensors, 22(3), 1242. DOI:https://doi.org/10.3390/s22031242
[2] Manikandan, N., Gobalakrishnan, N., & Pradeep, K. (2022). Bee optimization based random double adaptive whale optimization model for task scheduling in cloud computing environment. Computer communications, 187, 35–44. DOI:https://doi.org/10.1016/j.comcom.2022.01.016
[3] Imene, L., Sihem, S., Okba, K., & Mohamed, B. (2022). A third generation genetic algorithm NSGAIII for task scheduling in cloud computing. Journal of King Saud university-computer and information sciences, 34(9), 7515–7529. DOI:https://doi.org/10.1016/j.jksuci.2022.03.017
[4] Khan, M. S. A., & Santhosh, R. (2022). Task scheduling in cloud computing using hybrid optimization algorithm. Soft computing, 26(23), 13069–13079.
[5] Gupta, S., Iyer, S., Agarwal, G., Manoharan, P., Algarni, A. D., Aldehim, G., & Raahemifar, K. (2022). Efficient prioritization and processor selection schemes for heft algorithm: a makespan optimizer for task scheduling in cloud environment. Electronics, 11(16), 2557. DOI:https://doi.org/10.3390/electronics11162557
[6] Siddesha, K., Jayaramaiah, G. V, & Singh, C. (2022). A novel deep reinforcement learning scheme for task scheduling in cloud computing. Cluster computing, 25(6), 4171–4188. DOI:https://doi.org/10.1007/s10586-022-03630-2
[7] Algarni, A. (2022). A study on deep learning based parking lot allotment to the vehicles. Computational algorithms and numerical dimensions, 1(1), 46–51. DOI:10.22105/cand.2022.159983
[8] Mohapatra, H., & Rath, A. K. (2021). An iot based efficient multi-objective real-time smart parking system. International journal of sensor networks, 37(4), 219–232. DOI:10.1504/IJSNET.2021.119483
[9] Mohapatra, H., & Rath, A. K. (2019). Fault tolerance through energy balanced cluster formation (ebcf) in wsn. In Smart innovations in communication and computational sciences (pp. 313–321). Singapore: Springer Singapore.
[10] Bezdan, T., Zivkovic, M., Bacanin, N., Strumberger, I., Tuba, E., & Tuba, M. (2022). Multi-objective task scheduling in cloud computing environment by hybridized bat algorithm. Journal of intelligent and fuzzy systems, 42(1), 411–423. DOI:10.3233/JIFS-219200
[11] Mangalampalli, S., Swain, S. K., & Mangalampalli, V. K. (2022). Multi objective task scheduling in cloud computing using cat swarm optimization algorithm. Arabian journal for science and engineering, 47(2), 1821–1830.
[12] Abdullahi, M., Ngadi, M. A., Dishing, S. I., & Abdulhamid, S. M. (2023). An adaptive symbiotic organisms search for constrained task scheduling in cloud computing. Journal of ambient intelligence and humanized computing, 14(7), 8839–8850.
[13] Prity, F. S., Gazi, M. H., & Uddin, K. M. (2023). A review of task scheduling in cloud computing based on nature-inspired optimization algorithm. Cluster computing, 1–31. https://link.springer.com/article/10.1007/s10586-023-04090-y
[14] Gad, A. G., Houssein, E. H., Zhou, M., Suganthan, P. N., & Wazery, Y. M. (2023). Damping-assisted evolutionary swarm intelligence for industrial iot task scheduling in cloud computing. IEEE Internet of things jurnal, 1. https://ieeexplore.ieee.org/abstract/document/10171225/
[15] Mahmoud, H., Thabet, M., Khafagy, M. H., & Omara, F. A. (2022). Multiobjective task scheduling in cloud environment using decision tree algorithm. IEEE access, 10, 36140–36151. DOI:10.1109/ACCESS.2022.3163273
[16] Iftikhar, S., Ahmad, M. M. M., Tuli, S., Chowdhury, D., Xu, M., Gill, S. S., & Uhlig, S. (2023). HunterPlus: AI based energy-efficient task scheduling for cloud–fog computing environments. Internet of things, 21, 100667. DOI:https://doi.org/10.1016/j.iot.2022.100667
[17] Zhou, Z. (2023). Soil quality based agricultural activity through iot and wireless sensor network. Big data and computing visions, 3(1), 26–31. DOI:10.22105/bdcv.2022.332447.1056
[18] Nabi, S., Ahmad, M., Ibrahim, M., & Hamam, H. (2022). AdPSO: adaptive pso-based task scheduling approach for cloud computing. Sensors, 22(3), 920. https://www.mdpi.com/1424-8220/22/3/920
[19] Abualigah, L., & Alkhrabsheh, M. (2022). Amended hybrid multi-verse optimizer with genetic algorithm for solving task scheduling problem in cloud computing. The journal of supercomputing, 78(1), 740–765.
[20] Chhabra, A., Sahana, S. K., Sani, N. S., Mohammadzadeh, A., & Omar, H. A. (2022). Energy-aware bag-of-tasks scheduling in the cloud computing system using hybrid oppositional differential evolution-enabled whale optimization algorithm. Energies, 15(13), 4571. https://doi.org/10.3390/en15134571
[21] Ghafari, R., Kabutarkhani, F. H., & Mansouri, N. (2022). Task scheduling algorithms for energy optimization in cloud environment: a comprehensive review. Cluster computing, 25(2), 1035–1093.
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