AI-Optimized VMware Horizon VDI: Predictive Resource Scaling for GPU-Intensive Workloads in Hybrid Cloud Environments
Keywords:
VMware Horizon, GPU Resource Scaling, Hybrid Cloud, Machine Learning, Instant Clone, Cost-Performance OptimizationAbstract
This paper proposes an AI-driven framework for predictive GPU resource scaling in VMware Horizon Virtual Desktop Infrastructure (VDI) to optimize performance and cost-efficiency for GPU-intensive workloads like CAD, AI training, and medical imaging in hybrid cloud environments. By integrating machine learning (ML) models with VMware’s Instant Clone technology, the system dynamically forecasts GPU demand and provisions resources while balancing on-premises and public cloud infrastructure costs. A hybrid Long Short-Term Memory (LSTM) and Reinforcement Learning (RL) model achieves 92% prediction accuracy for GPU utilization, reducing idle resource costs by 35% compared to static allocation. Experimental results demonstrate a 40% improvement in workload latency and 28% savings in public cloud spending.
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