Improving Task Scheduling In Cloud Datacenters By Implementation Of An Intelligent Scheduling Algorithm

Omer K. Jasim Mohammad, Bassim M. Salih

Abstract


The need for mobile and online Apps and services has led to rapid growth in cloud computing services.  The rapid increase in growth highlights the importance of reducing the time required for scheduling and efficiently utilizing resources in a dynamic setting. Consequently, numerous scheduling algorithms have been devised to address these problems by employing intelligent scheduling approaches, including Genetic Algorithms, greedy algorithm, Antlion Optimizer, Ant Colony optimization, and Cuckoo Intelligent Algorithm. This study provides an overview of intelligent optimization strategies, with a specific focus on the Cuckoo intelligent methodology. Additionally, it presents a proposed implementation of a Cuckoo-based cloud computing environment as an efficient algorithm that is projected to yield improved results in task scheduling. 


Full Text:

PDF

References


Farrag, A. a. S., Mahmoud, S. A., & El-Horbaty, E. S. M, . Intelligent cloud algorithms for load balancing problems: A survey. 2015, 10.1109/IntelCIS.2015.7397223. https://doi.org/10.1109/intelcis.2015.7397223

Mohammad, O. K. J. , GALO:A new intelligent task scheduling algorithm in cloud computing environment. International Journal of Engineering & Technology, 7(4), 2088. 2018, https://doi.org/10.14419/ijet.v7i4.16486

Priya, & Babu, C. N. K., Moving average fuzzy resource scheduling for virtualized cloud data services. Computer Standards & Interfaces, 50, 251–257. 2018, https://doi.org/10.1016/j.csi.2016.10.011

R. Govindarajan, S. Meikandasivam, and D. Vijayakumar, “Performance Analysis of Smart Energy Monitoring Systems in Real-time”, Eng. Technol. Appl. Sci. Res., vol. 10, no. 3, pp. 5808–5813, Jun. 2020.

Zeng, L., Veeravalli, B., & Zomaya, A. Y., An integrated task computation and data management scheduling strategy for workflow applications in cloud environments. Journal of Network and Computer Applications, 50, 39–48. 2015, https://doi.org/10.1016/j.jnca.2015.01.001

Keshk, A., El-Sisi, A. B., & Tawfeek, M. A., Cloud Task Scheduling for Load Balancing based on Intelligent Strategy. International Journal of Intelligent Systems and Applications, 6(5), 25–36, 2015, https://doi.org/10.5815/ijisa.2014.05.02

Pradhan, R., & Satapathy, S. C., Particle Swarm Optimization-Based Energy-Aware task scheduling algorithm in heterogeneous cloud. In Lecture notes in networks and systems (pp. 439–450), 2022, https://doi.org/10.1007/978-981-19-4990-6_40

Varghese, B., & Buyya, R., Next generation cloud computing: New trends and research directions. Future Generation Computer Systems, 79, 849–861, 2018, https://doi.org/10.1016/j.future.2017.09.020

Calheiros, R. N., Ranjan, R., Beloglazov, A., De Rose, C. a. F., & Buyya, R., CloudSim: a toolkit for modeling and simulation of cloud computing environments and evaluation of resource provisioning algorithms. Software: Practice and Experience, 41(1), 23–50, 2010, https://doi.org/10.1002/spe.995

Tawfeek, M. A., El-Sisi, A. B., Keshk, A., & Torkey, F. A., Cloud task scheduling based on Ant Colony optimization. The International Arab Journal of Information Technology, Vol. 12, No. 2, March 2015, https://doi.org/10.1109/icces.2013.6707172

Abrishami, S., Naghibzadeh, M., & Epema, D., Deadline-constrained workflow scheduling algorithms for Infrastructure as a Service Clouds. Future Generation Computer Systems, 29(1), 158–169. 2018, https://doi.org/10.1016/j.future.2012.05.004

Ramezani, F., Lu, J., Taheri, J., & Hussain, F. K., Evolutionary algorithm-based multi-objective task scheduling optimization model in cloud environments. World Wide Web, 18(6), 1737–1757, 2015, https://doi.org/10.1007/s11280-015-0335-3

Bhoi, U., & Ramanuj, P. N. Enhanced Max-min Task Scheduling Algorithm in Cloud Computing.. Journal of Theoretical and Applied Information Technology, vol.2, issue 4,2013.

Topcuoglu, H. R., Hariri, S., & Wu, M, Performance-effective and low-complexity task scheduling for heterogeneous computing. IEEE Transactions on Parallel and Distributed Systems, 13(3), 260–274, 2002, https://doi.org/10.1109/71.993206

Fard, H. M., Prodan, R., & Fahringer, T., Multi-objective list scheduling of workflow applications in distributed computing infrastructures. Journal of Parallel and Distributed Computing, 74(3), 2152–2165, 2014, https://doi.org/10.1016/j.jpdc.2013.12.004

Moradbeiky, A., & Bardsiri, V., A Novel Task Scheduling Method in Cloud Environment using Cuckoo Optimization Algorithm. IJCS, 2(2), 7–20, 2014, https://doi.org/10.21742/ijcs.2015.2.2.02

Vijindra, & Shenai, S., Survey on scheduling issues in cloud Computing. Procedia Engineering, 38, 2881–2888, 2012, https://doi.org/10.1016/j.proeng.2012.06.337

Abadi, Z. J. K., & Mansouri, N., A comprehensive survey on scheduling algorithms using fuzzy systems in distributed environments. Artificial Intelligence Review, 57(1), 2024, https://doi.org/10.1007/s10462-023-10632-y

Ghafari, R., Kabutarkhani, F. H., & Mansouri, N., Task scheduling algorithms for energy optimization in cloud environment: a comprehensive review. Cluster Computing, 25(2), 1035–1093, 2022, https://doi.org/10.1007/s10586-021-03512-z

Murad, S. A., Muzahid, A. J. M., Azmi, Z. R. M., Hoque, M. I., & Kowsher, M., A review on job scheduling technique in cloud computing and priority rule based intelligent framework. Journal of King Saud University - Computer and Information Sciences, 34(6), 2309–2331, 2022, https://doi.org/10.1016/j.jksuci.2022.03.027

Walia, N. K., & Kaur, N., Performance analysis of the task scheduling algorithms in the cloud computing environments. 2021 2nd International Conference on Intelligent Engineering and Management (ICIEM), 2021, https://doi.org/10.1109/iciem51511.2021.9445320

Ajmal, M. S., Iqbal, Z., Khan, F. Z., Ahmad, M., Ahmad, I., & Gupta, B. B., Hybrid ant genetic algorithm for efficient task scheduling in cloud data centers. Computers & Electrical Engineering, 95, 107419, 2021, https://doi.org/10.1016/j.compeleceng.2021.107419

Kashikolaei, S. M. G., Hosseinabadi, A. a. R., Saemi, B., Shareh, M. B., Sangaiah, A. K., & Bian, G., An enhancement of task scheduling in cloud computing based on imperialist competitive algorithm and firefly algorithm. The Journal of Supercomputing, 76(8), 6302–6329, 2019, https://doi.org/10.1007/s11227-019-02816-7

Liu, H., Research on cloud computing adaptive task scheduling based on ant colony algorithm. Optik, 258, 168677, 2023, https://doi.org/10.1016/j.ijleo.2022.168677

Tang, C., Song, S., Ji, J., Tang, Y., Tang, Z., & Todo, Y., A cuckoo search algorithm with scale-free population topology, Expert Systems With Applications, 188, 116049, 2021, https://doi.org/10.1016/j.eswa.2021.116049

Rajabioun, R., Cuckoo Optimization Algorithm, Applied Soft Computing, 11(8), 5508–5518, 2012, https://doi.org/10.1016/j.asoc.2011.05.008

Sen, S., Li, J., Huang, Q., Xh, H., Shuang, K., & Jie, W., Cost-efficient task scheduling for executing large programs in the cloud. Parallel Computing, 39(4–5), 177–188, 2013, https://doi.org/10.1016/j.parco.2013.03.002

M. Ali, N. Q. Soomro, H. Ali, A. Awan, and M. Kirmani, “Distributed File Sharing and Retrieval Model for Cloud Virtual Environment”, Eng. Technol. Appl. Sci. Res., vol. 9, no. 2, pp. 4062–4065, Apr. 2019.

Wu, Z., Ni, Z., Gu, L., & Liu, X., A Revised Discrete Particle Swarm Optimization for Cloud Workflow Scheduling, 2010 International Conference on Computational Intelligence and Security, CIS 2010, 2010, https://doi.org/10.1109/cis.2010.46.

H. Reffad, A. Alti, and A. Almuhirat, “A Dynamic Adaptive Bio-Inspired Multi-Agent System for Healthcare Task Deployment”, Eng. Technol. Appl. Sci. Res., vol. 13, no. 1, pp. 10192–10198, Feb. 2023.




DOI: https://doi.org/10.31449/inf.v48i10.5843

Creative Commons License
This work is licensed under a Creative Commons Attribution 3.0 License.