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 rapid growth of big data applications has necessitated advanced computational frameworks capable of processing vast datasets efficiently. Distributed computing systems, enhanced by artificial intelligence (AI), offer promising solutions to address scalability, resource allocation, and performance optimization challenges. This article explores AI-driven optimization techniques for distributed computing systems tailored to big data applications. It reviews existing literature, proposes a novel AI-based optimization methodology, and evaluates its implementation through testing. The results demonstrate significant improvements in processing speed, resource utilization, and system scalability. The article concludes with insights into future research directions, emphasizing the integration of advanced AI algorithms and emerging hardware technologies to further enhance distributed systems.The rapid growth of big data applications has not only transformed the landscape of data processing but has also pushed the boundaries of traditional computational frameworks. As organizations grapple with exponentially increasing data volumes, the need for sophisticated distributed computing systems has become paramount. These systems, when augmented with artificial intelligence (AI), offer a powerful synergy that addresses the multifaceted challenges of scalability, resource allocation, and performance optimization. AI-driven optimization techniques have emerged as a critical component in enhancing the efficiency and effectiveness of distributed computing systems, particularly in the context of big data applications.

The integration of AI into distributed computing systems represents a paradigm shift in how large-scale data processing is approached. By leveraging machine learning algorithms and predictive analytics, these systems can dynamically adapt to changing workloads, optimize resource allocation in real-time, and improve overall system performance. This article delves into the intricacies of AI-driven optimization techniques, exploring how they can be tailored to meet the specific demands of big data applications. Through a comprehensive review of existing literature and the proposal of a novel AI-based optimization methodology, the article provides valuable insights into the current state of the field and potential future advancements. The implementation and testing of this methodology yield promising results, demonstrating tangible improvements in processing speed, resource utilization, and system scalability. These findings not only validate the efficacy of AI-driven approaches but also pave the way for future research directions, particularly in the integration of more advanced AI algorithms and emerging hardware technologies to further enhance the capabilities of distributed computing systems.

Keywords

• Artificial Intelligence, Distributed Computing, Big Data , Optimization , Machine Learning , Resource Allocation , Scalability

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