International Journal For Multidisciplinary Research

E-ISSN: 2582-2160   •   Impact Factor: 9.24

A Widely Indexed Open Access Peer Reviewed Multidisciplinary Bi-monthly Scholarly International Journal

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AI-Enhanced Compute Resource Management for Apache Spark: A Hybrid Approach Using Machine Learning Models and Large Language Models

Author(s) Seshendranath Balla Venkata
Country United States
Abstract This article presents a comprehensive framework for enhancing Apache Spark's compute resource
management capabilities through the integration of Machine Learning models and Large Language
Models. The proposed hybrid approach addresses the fundamental challenges of traditional compute
resource allocation methods by combining the predictive capabilities of ML with the natural language
understanding of LLMs. This article demonstrates how AI-driven compute resource management can
significantly improve cluster utilization, reduce operational overhead, and optimize cost efficiency
through detailed analysis of implementation strategies, performance metrics, and real-world applications.
The framework incorporates advanced feedback mechanisms, dynamic scaling capabilities, and intelligent
policy generation to create a robust and adaptive compute resource management system. The system
improves compute resource prediction accuracy, configuration optimization, and troubleshooting
efficiency by leveraging historical performance data, workload pattern recognition, and context-aware
compute resource recommendations. The integration of these technologies represents a significant
advancement in distributed computing compute resource management, offering organizations a powerful
solution for managing complex data processing workloads while maintaining high performance and
reliability standards.
Keywords Keywords: Compute Resource Management, Machine Learning, Large Language Models, Apache Spark, Distributed Computing
Field Computer
Published In Volume 6, Issue 6, November-December 2024
Published On 2024-12-25
DOI https://doi.org/10.36948/ijfmr.2024.v06i06.33716
Short DOI https://doi.org/g8w2ww
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