AI-Driven Optimization of Distributed Computing Systems for Big Data Applications

John A. Mathews¹, Priya S. Nair¹, Rahul Verma¹, and Dr. Ananya R. Sen²
¹Department of Computer Science and Engineering, TechnoVision Institute of Technology, Bengaluru, Karnataka, India
²Assistant Professor, Department of Computer Science and Engineering, TechnoVision Institute of Technology, Bengaluru, Karnataka, India

Abstract

The swift expansion of big data applications has created a demand for sophisticated computational frameworks that can efficiently handle large datasets. Distributed computing systems, when combined with artificial intelligence (AI), present promising solutions to tackle issues related to scalability, resource management, and performance enhancement. This article delves into AI-driven optimization strategies for distributed computing systems specifically designed for big data applications. It examines current literature, introduces a new AI-based optimization approach, and assesses its implementation through testing. The findings reveal notable advancements in processing speed, resource efficiency, and system scalability. The article concludes by offering insights into future research directions, highlighting the importance of integrating advanced AI algorithms and new hardware technologies to further improve distributed systems. The rapid expansion of big data applications has not only reshaped data processing but also challenged the limits of traditional computational frameworks. As organizations face ever-growing data volumes, the necessity for advanced distributed computing systems has become crucial. These systems, when enhanced with AI, create a powerful synergy that addresses the complex challenges of scalability, resource management, and performance optimization. AI-driven optimization techniques have become essential in boosting the efficiency and effectiveness of distributed computing systems, especially in the realm of big data applications.

The incorporation of artificial intelligence into distributed computing systems marks a significant transformation in the approach to large-scale data processing. By utilizing machine learning algorithms and predictive analytics, these systems can adjust dynamically to fluctuating workloads, optimize resource distribution in real-time, and enhance overall system efficiency. This article examines the complexities of AI-driven optimization strategies, investigating how they can be customized to address the unique requirements of big data applications. Through an extensive review of current literature and the introduction of a new AI-based optimization approach, the article offers valuable insights into the present state of the field and potential future developments. The application and evaluation of this approach produce encouraging outcomes, showing clear improvements in processing speed, resource usage, and system scalability. These results not only confirm the effectiveness of AI-driven methods but also set the stage for future research, especially in integrating more sophisticated AI algorithms and new hardware technologies to further boost the capabilities of distributed computing systems.

Keywords

• AI, Distributed Systems, Large-Scale Data, Optimization Techniques, Machine Learning, Resource Management, Scalability

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