Artificial Intelligence Integration for Sustainable High-Performance Cloud Networking Virtualization
Harish Chamarthi
Abstract
The exponential growth of cloud computing has escalated global energy consumption and carbon emissions from data centers, presenting critical environmental and operational challenges. This paper proposes an artificial intelligence (AI)-driven framework for sustainable high-performance cloud networking virtualization that optimizes energy efficiency while maintaining quality of service. Our methodology integrates machine learning algorithms for dynamic resource allocation, predictive workload management, and carbon-aware computing. We introduce a novel query fragment caching algorithm that reduces computational overhead by 32% compared to conventional approaches. Through simulation of real-world workload traces, our model demonstrates a 41.5% reduction in energy consumption and a 38.2% decrease in carbon emissions while maintaining 99.2% of performance benchmarks. The architecture employs deep reinforcement learning for adaptive resource provisioning and federated learning techniques to minimize data transmission costs. These findings substantiate that AI integration enables substantial sustainability improvements in cloud networking without compromising performance, representing a significant advancement toward environmentally responsible digital infrastructure.