Deep Learning-Based Optimization Techniques For Large-Scale Data Processing In Cloud Environments

Abstract

The exponential growth of large-scale datasets in modern industries—ranging from social media and e-commerce to bioinformatics and IoT—has increased the demand for efficient cloud-based data processing platforms. Deep learning-powered optimization techniques offer powerful solutions for handling the computational complexity, latency challenges, and resource allocation demands of distributed cloud systems. This article provides an in-depth exploration of deep learning models used for optimizing data processing tasks, including neural scheduling algorithms, reinforcement learning-based resource allocation, auto-scaling prediction models, and deep neural network–driven workload balancing. Two graphs demonstrate the efficiency improvements and reduction in processing latency offered by deep learning integration. The article concludes with key challenges such as energy consumption, model transparency, and data security, while outlining future research directions involving federated optimization, edge–cloud collaboration, and quantum-assisted deep learning.