Predictive Cloud Region Selection for Generative AI: Forecasting Regional Cost, Latency, Carbon Intensity, and Resource Availability Before Computational Demand Materializes

The increasing deployment of generative artificial intelligence (AI) systems has created geographically distributed computational workloads characterized by heterogeneous GPU infrastructure, fluctuating cloud instance prices, variable network latency, changing electricity carbon intensity, and uncertain regional resource availability. Although existing cloud resource allocation and geographical load-balancing approaches can distribute computational workloads across multiple regions, their decisions are frequently based on current resource conditions, predefined deployment policies, or reactive workload placement. Such approaches may overlook opportunities to anticipate future regional operating conditions before computational demand materializes. This paper introduces Predictive Cloud Region Selection for Generative AI (PCRS-GAI), a conceptual framework that investigates how future computational workloads can be assigned to geographically distributed cloud regions according to predicted economic, network, environmental, and infrastructure conditions. Rather than selecting a cloud region solely according to its current GPU price, geographical proximity, or available capacity, PCRS-GAI jointly forecasts future GPU instance prices, request-to-region network latency, electricity carbon intensity, and regional computational resource availability. Its architecture integrates future workload characterization, regional condition forecasting, multi-objective region evaluation, predictive placement decisions, capacity reservation, and adaptive deployment control. A unified optimization formulation is developed to select appropriate execution regions while accounting for GPU rental expenditure, network communication costs, latency requirements, carbon emissions, resource availability, data-transfer overhead, regional capacity constraints, and prediction uncertainty. The framework further distinguishes between cloud instance pricing and electricity-related environmental costs, recognizing that commercial GPU rental prices do not necessarily reflect the temporal carbon intensity of the electricity consumed by the underlying infrastructure. It also considers the possibility that a region offering favorable prices and carbon intensity may have insufficient GPU capacity when a predicted workload actually arrives. An experimental methodology is established to evaluate predictive regional selection against static region assignment, nearest-region placement, lowest-current-price selection, and conventional reactive geographical load balancing. The principal evaluation criteria include total computational expenditure, request latency, carbon emissions, regional resource utilization, placement accuracy, and service-level reliability. The proposed framework does not assume that predictive regional selection is universally advantageous; its effectiveness depends on regional condition predictability, geographic demand characteristics, resource heterogeneity, workload mobility, and the accuracy of future infrastructure forecasts. By extending predictive computing from workload preparation and economic scheduling to geographically informed computational placement, this research investigates a further dimension of proactive infrastructure orchestration for generative AI.