Optimized Large-Scale Data Analytics Using Advanced Computational Methods in Cloud Computing Frameworks

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.