Optimized Large-Scale Data Analytics Using Advanced Computational Methods in Cloud Computing Frameworks
DOI:
https://doi.org/10.59461/ijdiic.v5i2.273Keywords:
Large-Scale Data Analytics, Computational Methods, Cloud Computing, Workload Distribution, Capsule Networks for Cloud AnalyticsAbstract
In today's data-driven world, the exponential growth of information from diverse sources necessitates robust, scalable, and efficient computational solutions. Large-scale data analytics is pivotal in extracting valuable insights from massive datasets, facilitating informed decision-making across industries. This research proposes an advanced computational framework that integrates Deep Learning (DL) techniques to optimize large-scale data analytics in cloud computing environments. The proposed system leverages the Adaptive Satin Bowerbird Optimizer-driven Dynamic Capsule Network (ASB-Dynamic-CapsNet) to automate workload distribution while minimizing execution time and resource utilization. The data collection process involves gathering large-scale datasets from multiple sources, including cloud platforms, Internet of Things (IoT) devices, transactional systems, social media, and enterprise databases. Preprocessing steps like handling missing values and standardizing data ensure data quality and consistency. The framework is evaluated using key performance metrics like Central Processing Unit (CPU) utilization (86%), Random Access Memory (RAM) utilization (68%), and training accuracy (95.9%). Experimental results demonstrate that the ASB-Dynamic-CapsNet model outperforms alternative scheduling approaches and significantly improves the performance of CPU, RAM, and training accuracy compared to traditional algorithms. Overall, the findings highlight the efficacy of DL-driven scheduling in optimizing cloud-based data analytics, providing a scalable and efficient solution for high-volume workloads in modern computing environments.
References
A. H. A. AL-Jumaili, R. C. Muniyandi, M. K. Hasan, J. K. S. Paw, and M. J. Singh, “Big Data Analytics Using Cloud Computing Based Frameworks for Power Management Systems: Status, Constraints, and Future Recommendations,” Sensors, vol. 23, no. 6, p. 2952, Mar. 2023, doi: 10.3390/s23062952.
Y. Kumar, J. Marchena, A. H. Awlla, J. J. Li, and H. B. Abdalla, “The AI-Powered Evolution of Big Data,” Appl Sci, vol. 14, no. 22, p. 10176, Nov. 2024, doi: 10.3390/app142210176.
J. Govea, E. Ocampo Edye, S. Revelo-Tapia, and W. Villegas-Ch, “Optimization and Scalability of Educational Platforms: Integration of Artificial Intelligence and Cloud Computing,” Computers, vol. 12, no. 11, p. 223, Nov. 2023, doi: 10.3390/computers12110223.
V. K. Prasad, D. Dansana, M. D. Bhavsar, B. Acharya, V. C. Gerogiannis, and A. Kanavos, “Efficient Resource Utilization in IoT and Cloud Computing,” Information, vol. 14, no. 11, p. 619, Nov. 2023, doi: 10.3390/info14110619.
K. Bajaj, B. Sharma, and R. Singh, “Implementation analysis of IoT-based offloading frameworks on cloud/edge computing for sensor generated big data,” Complex Intell Syst, vol. 8, no. 5, pp. 3641–3658, Oct. 2022, doi: 10.1007/s40747-021-00434-6.
S. M. Seyyedsalehi and M. Khansari, “Virtual Machine Placement Optimization for Big Data Applications in Cloud Computing,” IEEE Access, vol. 10, pp. 96112–96127, 2022, doi: 10.1109/ACCESS.2022.3203057.
M. Q. Bashabsheh, L. Abualigah, and M. Alshinwan, “Big Data Analysis Using Hybrid Meta-Heuristic Optimization Algorithm and MapReduce Framework,” 2022, pp. 181–223. doi: 10.1007/978-3-030-99079-4_8.
S. T. V. Sresth, S. P. Nagavalli, “Optimizing data pipelines in advanced cloud computing: Innovative approaches to large-scale data processing, analytics, and real-time optimization,” Int J Res Anal Rev, vol. 10, no. 2023, pp. 478–496.
Ü. Demirbaga, G. S. Aujla, A. Jindal, and O. Kalyon, “Cloud Computing for Big Data Analytics,” in Big Data Analytics, Cham: Springer Nature Switzerland, 2024, pp. 43–77. doi: 10.1007/978-3-031-55639-5_4.
L. Hu, S. Yang, X. Luo, H. Yuan, K. Sedraoui, and M. Zhou, “A Distributed Framework for Large-scale Protein-protein Interaction Data Analysis and Prediction Using MapReduce,” IEEE/CAA J Autom Sin, vol. 9, no. 1, pp. 160–172, Jan. 2022, doi: 10.1109/JAS.2021.1004198.
M. Adhikari and H. Gianey, “Energy efficient offloading strategy in fog-cloud environment for IoT applications,” Internet of Things, vol. 6, p. 100053, Jun. 2019, doi: 10.1016/j.iot.2019.100053.
F. M. Awaysheh, M. N. Aladwan, M. Alazab, S. Alawadi, J. C. Cabaleiro, and T. F. Pena, “Security by Design for Big Data Frameworks Over Cloud Computing,” IEEE Trans Eng Manag, vol. 69, no. 6, pp. 3676–3693, Dec. 2022, doi: 10.1109/TEM.2020.3045661.
X. Wang et al., “Reproducible and Portable Big Data Analytics in the Cloud,” IEEE Trans Cloud Comput, vol. 11, no. 3, pp. 2966–2982, Jul. 2023, doi: 10.1109/TCC.2023.3245081.
A. K. Alkhalifa et al., “Hybrid dung beetle optimization based dimensionality reduction with deep learning based cybersecurity solution on IoT environment,” Alexandria Eng J, vol. 111, pp. 148–159, Jan. 2025, doi: 10.1016/j.aej.2024.10.053.
M. Costa, D. Costa, T. Gomes, and S. Pinto, “Shifting Capsule Networks from the Cloud to the Deep Edge,” ACM Trans Intell Syst Technol, vol. 13, no. 6, pp. 1–25, Dec. 2022, doi: 10.1145/3544562.
G. Rjoub, J. Bentahar, O. Abdel Wahab, and A. Saleh Bataineh, “Deep and reinforcement learning for automated task scheduling in large‐scale cloud computing systems,” Concurr Comput Pract Exp, vol. 33, no. 23, Dec. 2021, doi: 10.1002/cpe.5919.
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