AI-Driven Computational Resource Optimization: A Hybrid Deep Reinforcement Learning Framework for Enhancing Large-Scale Model Efficiency

Xiaoying Li

doi:10.71222/ke7c3143

Authors

Xiaoying Li Carnegie Mellon University, Mountain View, CA, USA Author

DOI:

https://doi.org/10.71222/ke7c3143

Keywords:

computational resource optimization, model hardware scheduling, deep reinforcement learning, large-scale model efficiency

Abstract

This paper presents an AI-driven approach to computational resource optimization aimed at enhancing efficiency and resource utilization in large-scale AI model training and inference processes. As AI models continue to grow exponentially in size, computational resource demands have increased dramatically, with studies indicating that resource utilization efficiency in large model training and deployment typically falls below 60%, resulting in significant cost waste and energy consumption. The proposed framework leverages reinforcement learning and predictive analytics techniques to implement intelligent resource allocation and task scheduling across di-verse hardware environments (GPUs, CPUs, specialized accelerators). The multi-layered architecture incorporates four core components: resource monitoring, workload prediction, adaptive scheduling, and dynamic optimization, capable of adjusting resource configurations dynamically based on workload characteristics and hardware capabilities. Experimental evaluation across 32 large-scale AI training and inference scenarios demonstrated an average 38.7% throughput improvement and 42.3% energy consumption reduction compared to traditional scheduling meth-odds. Field validation through four industry case studies further confirmed the practical value of this approach in financial services, e-commerce, healthcare, and industrial sectors, achieving an average 31.2% increase in resource utilization and 24.8% reduction in operational costs. Economic analysis indicates substantial return on investment (169% average over 6 months) through improved computational efficiency and reduced infrastructure expenses. These research findings have significant strategic implications for enhancing core competitiveness in high-performance computing and AI infrastructure, contributing to reduced dependency on third-party computational resources, accelerated AI innovation cycles, and advancement of green computing initiatives.

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