An NLP-Based Approach for Optimising Task Scheduling in Cloud Computing using Different Meta-Heuristic Algorithms
Keywords:
Cloud Computing (CC), Machine Learning (ML) Intelligence, Optimal Task Scheduling, Natural Language Processing (NLP.)Abstract
Organizing tasks efficiently is crucial for optimizing the performance of cloud computing. Our research study introduces a novel ML-based method to assess and rank algorithms for task scheduling, taking into account their characteristics. Through the utilization of Google Drive and the SpaCy English model for data extraction, we detect and measure significant descriptive terms associated with algorithm features. By assigning priorities based upon our algorithm to these terms espically based on their frequencies, we are able to determine the relative significance of each feature. By amalgamating these priorities, we calculate priority scores for each algorithm, unveiling their potential for performance. The optimal task scheduling algorithm can be known by analyzing the priority scores. By showing these scores using a X-Y plot helps users easily understand and compare the different algorithms. Our unique approach allows personal or enterprise users to make informed decisions, thereby optimizing the utilization of cloud resources and improving overall efficiency. This research study proposes an Natural Language Processing -driven methodology supported by data to navigate various task scheduling algorithms, ultimately enhancing cloud computing performance. The power of artificial intelligence immensely helps to achieve better resource utilization and improved performance in cloud environments.
Downloads
References
Duan, C., Wang, Y., Chen, W., Chen, W., Yuan, J., & Zhou, H. (2022). "A Parallel Task Scheduling Method Based on Ant Colony Algorithm in Cloud Computing." Sensors, 22(3), 1242. (https://www.mdpi.com/1424-8220/22/3/1242)
Yu, S., Zhang, Y., & Lan, X. (2020). "Task scheduling algorithm for energy-aware cloud computing based on modified fireworks algorithm." Journal of Parallel and Distributed Computing, 135, 86-92. (https://www.sciencedirect.com/science/article/pii/S0140366419312101)
Sun, Z., Xu, X., & Wang, Y. (2019). "A Genetic Algorithm-Based Task Scheduling Strategy for Cloud Computing." In International Conference on Cloud Computing and Big Data Analysis (pp. 191-199). Springer, Cham.(https://link.springer.com/chapter/10.1007/978-3-030-19223-5_13)
Pande, M., & Dongre, S. (2023). "An enhanced genetic algorithm for task scheduling in cloud computing." Journal of Cloud Computing: Advances, Systems and Applications, 12(1), 1-21. AvMLlable: [Link to the paper](https://journalofcloudcomputing.springeropen.com/articles/10.1186/s13677-023-00402-0)
Abolfazli, S., Sanaei, Z., Ahmed, E., Gani, A., & Buyya, R. (2016). "Cloud-based augmentation for mobile devices: motivation, taxonomies, and open challenges." ACM Computing Surveys (CSUR), 48(1), 8. AvMLlable:
[Link to the paper](https://www.sciencedirect.com/science/article/pii/S2214785320367535)
Das, A., Muthurajkumar, S., & Balasubramanian, S. (2022). "An energy-efficient task scheduling algorithm for mobile cloud computing using particle swarm optimization." In Proceedings of the 7th International Conference on Computing and Network Communications (pp. 129-135). AvMLlable: [Link to the paper](https://www.sciencedirect.com/science/article/pii/S221053792200111)
Benlian, A., Kettinger, W. J., Sunyaev, A., Winkler, T. J., & Guest Editors. (2018). "The transformative value of cloud computing: a decoupling, platformization, and recombination theoretical framework." Journal of management information systems, 35(3), 719-739. (https://www.tandfonline.com/doi/full/10.1080/07421222.2018.1494404)
Nanda Banger, P. (2022). "A Review Paper on Cloud Computing Architecture, Types, Advantages and Disadvantages." International Journal of Advanced Research in Science, Communication and Technology (IJARSCT), 2(2). (https://ijarsct.com/volume-2-issue-2-2022/)
Almubaddel, M., & Elmogy, A. M. (2016). "Cloud computing antecedents, challenges, and directions." In Proceedings of the International Conference on Internet of things and Cloud Computing (pp. 1-5). AvMLlable: [Link to the paper](https://ieeexplore.ieee.org/document/7580426)
Kak, S. M., Agarwal, P., & Alam, M. A. (2022). "Task Scheduling Techniques for Energy Efficiency in the Cloud." EML Endorsed Transactions on Energy Web, 9(39), e6-e6. (https://eudl.eu/doi/10.4108/eML.5-7-2022.172686)
Srichandan, S., Kumar, T. A., & Bibhudatta, S. (2018). "Task scheduling for cloud computing using multi-objective hybrid bacteria foraging algorithm." Future Computing and Informatics Journal, 3(2), 210-230. (https://content.iospress.com/articles/future-computing-and-informatics-journal/fci176)
Khatoon, A., & Bansal, J. C. (2012). "A Survey on Cloud Computing." International Journal of Computer Applications, 53(5), 9-16.(https://www.sciencedirect.com/science/article/pii/S0167739X11001713)
Efe, K. (2006). "Heuristic models of task assignment scheduling in distributed systems." In Proceedings of the International Conference on Parallel and Distributed Processing Techniques and Applications (pp. 240-246). (https://www.researchgate.net/profile/Kemal-Efe-2/publication/2958803_Heuristic_Models_of_Task_Assignment_Scheduling_in_Distributed_Systems/links/02e7e53c2d72ca61c9000000/Heuristic-Models-of-Task-Assignment-Scheduling-in-Distributed-Systems.pdf)
Harchol, Y., & Raz, D. (2018). "Algorithmic information theory in scheduling problems." Operations Research Letters, 46(2), 231-234.(https://www.sciencedirect.com/science/article/pii/S0098135418300449)
Adhikari, A., & Ganguli, R. (2013). "Time-constrMLned task scheduling using genetic algorithm for cloud computing environment." In Proceedings of the International Conference on Advances in Computing, Communications and Informatics (pp. 1127-1131).(https://depositonce.tu-berlin.de/bitstreams/3442c05d-66f3-47fb-be95-687571b8c6a4/download)
Miller, E. (2021). "Leveraging the Cloud: A Data Science Approach." arXiv preprint arXiv:2109.09138. (https://arxiv.org/abs/2109.09138)
Andrieu, C., Freitas, N., & Doucet, A. (2002). "An adaptive metropolis algorithm." Bernoulli, 8(5), 1009-1038. (https://ieeexplore.ieee.org/abstract/document/9978606/?casa_token=P42NAADmyXEAAAAA:CiGKRZIoJs5lR43Sm-H_yf9sAdc3AXLi58MK5m3NOKIyWPe2S1fy5ROplEOnG6V9SrkJ4aEO1nw)
Issac, B. (2014). "Adaptive time quantum for task scheduling in cloud computing environment." In Proceedings of the International Conference on Communication and Signal Processing (pp. 701-705). (https://ieeexplore.ieee.org/abstract/document/7019847/?casa_token=sheAAoVHLHQAAAAA:fOugp7GAEy2lWApS2KOJ8suBn_uTijqcNS58_lpyNVVAZLYNM-NllV2tss5Ldvme1yHsoX8YTMM)
Rajput, N., Shah, K., & Thakur, R. (2019). "Resource scheduling in cloud computing: taxonomy, challenges, and state-of-the-art." Journal of Cloud Computing: Advances, Systems and Applications, 8(1), 1-40. (https://www.sciencedirect.com/science/article/pii/S0140366419312101)
Choudhary, S., & Mishra, A. (2014). "Task scheduling algorithm for cloud computing using particle swarm optimization." International Journal of Computer Applications, 97(7), 6-11. (http://www.ijcMLt.com/IJCMLT/92/924.pdf)
M. Chhabra and S. Basheer,(2022) "Recent Task Scheduling-based Heuristic and Meta-heuristics Methods in Cloud Computing: A Review," 2022 5th International Conference on Contemporary Computing and Informatics (IC3I), Uttar Pradesh, India, 2022, pp. 2236-2242, doi: 10.1109/IC3I56241.2022.10073445.
Explosion AI. (2021). spaCy 3.0: Industrial-strength Natural Language Processing in Python. Retrieved from https://spaCy.io/models
Downloads
Published
How to Cite
Issue
Section
License
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.
All papers should be submitted electronically. All submitted manuscripts must be original work that is not under submission at another journal or under consideration for publication in another form, such as a monograph or chapter of a book. Authors of submitted papers are obligated not to submit their paper for publication elsewhere until an editorial decision is rendered on their submission. Further, authors of accepted papers are prohibited from publishing the results in other publications that appear before the paper is published in the Journal unless they receive approval for doing so from the Editor-In-Chief.
IJISAE open access articles are licensed under a Creative Commons Attribution-ShareAlike 4.0 International License. This license lets the audience to give appropriate credit, provide a link to the license, and indicate if changes were made and if they remix, transform, or build upon the material, they must distribute contributions under the same license as the original.
