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

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.