Big Data Advanced Scheduling Integrating Q-Learning and Cuckoo Search
Keywords:
Cloud Computing, Task Scheduling, Big Data, Machine Learning, Advanced Cuckoo search, Feed Forward Neural Network (FFNN).Abstract
As a well-known fact sensor nodes are collecting enormous amounts of data in real-time, task scheduling is a crucial problem in big data. It is essential in determining the effectiveness and performance of networks. The current methods only address the node angle that degrades the system's performance. This research presents two distinct frameworks to which a flexible Q-learning technique has been offered. The first scheduling architecture selects the suitable data node based on buffer latency using a Q-learning algorithm. In the second, feed forward neural networks are used to train the system while the reward mechanism is employed. To maintain the best records, the Advanced Cuckoo Search (A-CS) technique, a form of swarm intelligence, has been employed in conjunction with the K-means clustering algorithm to determine the centroid. Accuracy is 93.25%, True Positive Rate (TPR) is 0.93, and False Positive Rate (FPR) is 0.07. These performance metrics have been measured. The results indicate that the accuracy of the suggested strategy has increased by 6%, 5%, and 12%, respectively, when compared to the random forest, naïve bayes, and multi-class support vector machine.
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F. Marozzo and D. Talia, “Perspectives on Big Data, Cloud-Based Data Analysis and Machine Learning Systems,” Big Data and Cognitive Computing 2023, Vol. 7, Page 104, vol. 7, no. 2, p. 104, May 2023, doi: 10.3390/BDCC7020104.
D. Soni, D. Srivastava, A. Bhatt, A. Aggarwal, S. Kumar, et.al., An Empirical Client Cloud Environment to Secure Data Communication with Alert Protocol. Mathematical Problems in Engineering, 2022.
I. M. Ibrahim, et.al., “Task scheduling algorithms in cloud computing: A review,” Turkish Journal of Computer and Mathematics Education (TURCOMAT), vol. 12, no. 4, pp. 1041–1053, 2021.
D. Paulraj, T. Sethukarasi, S. Neelakandan, M. Prakash Id, and E. Baburaj Id, “An Efficient Hybrid Job Scheduling Optimization (EHJSO) approach to enhance resource search using Cuckoo and Grey Wolf Job Optimization for cloud environment,” PLOS ONE, vol. 18, no. 3, p. e0282600, Mar. 2023, doi: 10.1371/JOURNAL.PONE.0282600.
A. Agarwal, N. Bora, and N. Arora. "Goodput enhanced digital image watermarking scheme based on DWT and SVD." International Journal of Application or Innovation in Engineering & Management vol. 2, no. 9, pp. 36-41, 2013.
H. A. Alsayadi, N. Khodadadi, and S. Kumar. "Improving the Regression of Communities and Crime Using Ensemble of Machine Learning Models." Journal of Artificial Intelligence and Metaheuristics vol. 1, no. 1, pp. 27-34, 2022.
V. K. Chandarapu and M. Kasa, “Balanced Prediction Based Dynamic Resource Allocation Model for Online Big Data Streams using Historical Data,” International Journal of Intelligent Systems and Applications in Engineering, vol. 10, no. 2s, pp. 81–87, Dec. 2022, Accessed: Nov. 17, 2023. [Online]. Available: https://www.ijisae.org/index.php/IJISAE/article/view/2366
X. Fu, Y. Sun, H. Wang, and H. Li, “Task scheduling of cloud computing based on hybrid particle swarm algorithm and genetic algorithm,” Cluster Computing, pp. 1–10, 2021.
K. Kaur, S. Garg, G. Kaddoum, and N. Kumar, “Energy and SLA-driven MapReduce job scheduling framework for cloud-based cyber-physical systems,” ACM Transactions on Internet Technology (TOIT), vol. 21, no. 2, pp. 1–24, 2021.
A. Aggarwal, S. Kumar, A. Bhatt, and M. A. Shah. "Solving user priority in cloud computing using enhanced optimization algorithm in workflow scheduling." Computational Intelligence and Neuroscience 2022.
V. Vashishth, A. Chhabra, and A. Sood, “A predictive approach to task scheduling for Big Data in cloud environments using classification algorithms,” in 2017 7th International Conference on Cloud Computing, Data Science & Engineering-Confluence, 2017, pp. 188–192.
L. Zhou, S. Pan, J. Wang, and A. V Vasilakos, “Machine learning on big data: Opportunities and challenges,” Neurocomputing, vol. 237, pp. 350–361, 2017.
A. Aggarwal, P. Dimri, and A. Agarwal. "Survey on scheduling algorithms for multiple workflows in cloud computing environment." International Journal on Computer Science and Engineering, vol. 7, no. 6, pp. 565-570, 2019.
[14] O. Y. Mohammed, H. I. Abed, and N. A. Sultan, “Design and Implementation of Machine Learning and Big Data Analytics models for Cloud Computing platforms,” International Journal of Intelligent Systems and Applications in Engineering, vol. 11, no. 6s, pp. 185–192, May 2023, Accessed: Nov. 17, 2023. [Online]. Available: https://www.ijisae.org/index.php/IJISAE/article/view/2840
I. M. Alqahtani, E. Shadadi, and L. Alamer, “Big Data and Reality Mining in Healthcare Smart Prediction of Clinical Disease Using Decision Tree Classifier,” International Journal of Intelligent Systems and Applications in Engineering, vol. 10, no. 4, pp. 487–492, Dec. 2022, Accessed: Nov. 17, 2023. [Online]. Available: https://ijisae.org/index.php/IJISAE/article/view/2312
R. K. Jena, “Multi objective task scheduling in cloud environment using nested PSO framework,” Procedia Computer Science, vol. 57, pp. 1219–1227, 2015.
Q. Tian et al., “A hybrid task scheduling algorithm based on task clustering,” Mobile Networks and Applications, vol. 25, no. 4, pp. 1518–1527, 2020.
A. Alhubaishy and A. Aljuhani, “The best-worst method for resource allocation and task scheduling in cloud computing,” in 2020 3rd International Conference on Computer Applications & Information Security (ICCAIS), 2020, pp. 1–6.
G. Rjoub, J. Bentahar, and O. A. Wahab, “BigTrustScheduling: Trust-aware big data task scheduling approach in cloud computing environments,” Future Generation Computer Systems, vol. 110, pp. 1079–1097, 2020, doi: 10.1016/j.future.2019.11.019.
D. Ding, X. Fan, Y. Zhao, K. Kang, Q. Yin, and J. Zeng, “Q-learning based dynamic task scheduling for energy-efficient cloud computing,” Future Generation Computer Systems, vol. 108, pp. 361–371, 2020.
Z. Tong, H. Chen, X. Deng, K. Li, and K. Li, “A scheduling scheme in the cloud computing environment using deep Q-learning,” Information Sciences, vol. 512, pp. 1170–1191, 2020.
M. S. Sanaj and P. M. J. Prathap, “An efficient approach to the map-reduce framework and genetic algorithm based whale optimization algorithm for task scheduling in cloud computing environment,” Materials Today: Proceedings, vol. 37, pp. 3199–3208, 2021.
Z. Jalalian and M. Sharifi, “A hierarchical multi-objective task scheduling approach for fast big data processing,” The Journal of Supercomputing, vol. 78, no. 2, pp. 2307–2336, 2022.
N. Arunadevi and V. Thulasiraaman, “Cuckoo Search Augmented MapReduce for Predictive Scheduling With Big Stream Data,” International Journal of Sociotechnology and Knowledge Development (IJSKD), vol. 14, no. 1, pp. 1–18, 2022.
K. Loheswaran, T. Daniya, and K. Karthick, “Hybrid cuckoo search algorithm with iterative local search for optimized task scheduling on cloud computing environment,” Journal of Computational and Theoretical Nanoscience, vol. 16, no. 5–6, pp. 2065–2071, 2019.
J. García, F. Altimiras, A. Peña, G. Astorga, and O. Peredo, “A binary cuckoo search big data algorithm applied to large-scale crew scheduling problems,” Complexity, vol. 2018, 2018, doi: 10.1155/2018/8395193.
M. Agarwal and G. M. S. Srivastava, “A Cuckoo Search Algorithm-Based Task Scheduling in Cloud Computing,” Advances in Intelligent Systems and Computing, vol. 554, pp. 293–299, 2018, doi: 10.1007/978-981-10-3773-3_29.
H. Pan, Y. Lei, and S. Yin, “K-means clustering algorithm for data distribution in cloud computing environment,” International Journal of Grid and Utility Computing, vol. 12, no. 3, pp. 322–331, 2021.
Dhabliya, D., Ugli, I.S.M., Murali, M.J., Abbas, A.H.R., Gulbahor, U. Computer Vision: Advances in Image and Video Analysis (2023) E3S Web of Conferences, 399, art. no. 04045, .
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