Optimizing Big Data Processing Using AI-Driven Distributed Computing Architectures for Enhanced Scalability and Performance

Alan Crishtoper

Authors

Alan Crishtoper USA Author

Keywords:

Big Data, Artificial Intelligence, Distributed Computing, Scalability, Performance Optimization, Deep Learning, Cloud Computing

Abstract

The exponential growth of data has led to an increased demand for efficient big data processing techniques. Traditional distributed computing architectures face challenges in handling high-volume, high-velocity data while maintaining scalability and performance. This paper explores the integration of artificial intelligence (AI) in optimizing distributed computing architectures to improve efficiency, reduce computational overhead, and enhance scalability. By leveraging AI-driven techniques such as deep learning, reinforcement learning, and automated resource management, modern distributed systems can dynamically allocate resources, predict workload patterns, and optimize data processing pipelines. A comparative analysis of AI-optimized and conventional big data processing frameworks is conducted to highlight performance gains. The study also examines the role of AI in fault tolerance, task scheduling, and real-time analytics. The findings demonstrate that AI-driven architectures significantly improve data processing efficiency, making them ideal for handling large-scale workloads in cloud and edge computing environments.

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