Abstract

Rapidly expanding data centers along with networks create a fundamental problem regarding resource allocation efficiency. Standard resource management systems prove unable to adapt dynamically to varying workloads so bandwidth allocation and computing utilization stays inefficient. Developers use recent advancements in artificial intelligence technology to build automatic optimization algorithms that instantly adjust resource distributions. Through the integration of machine learning with deep reinforcement learning systems organizations obtain predictive power to prepare resource distribution ahead of time without endangering operational efficiency. According to Gandhi et al. (2012), both tested methods achieve enhanced energy efficiency while preventing delivery slowness. The study explores how AI addresses data facility network resource optimization by examining key techniques and discovering current trends and future development directions.