Predictive Computing Simulation Environment for Generative AI: Modeling Heterogeneous Request Distributions, Hardware Configurations, and Dynamic Computational Pricing

The development of predictive computing for generative artificial intelligence (AI) has introduced new opportunities for anticipating computational demand, preparing inference resources, and optimizing the economic efficiency of large language model (LLM) services. Existing predictive computing research has investigated future request prediction, proactive computational preparation, predictive infrastructure orchestration, and economic-aware resource scheduling. However, evaluating these approaches under a single workload, hardware configuration, or pricing environment may provide an incomplete understanding of their practical effectiveness. Predictive computational strategies are inherently sensitive to environmental conditions, including request arrival distributions, workload characteristics, GPU capabilities, memory limitations, electricity prices, and cloud resource costs. A strategy that provides benefits under one operating condition may become inefficient or even counterproductive under another. This paper introduces the Predictive Computing Simulation Environment for Generative AI (PCSE-GAI), a configurable simulation framework designed to model heterogeneous computational environments and systematically evaluate predictive computing strategies under diverse operating conditions. The proposed environment integrates five interconnected simulation components: (1) synthetic and trace-driven request workload generation, (2) predictive demand and forecasting simulation, (3) heterogeneous hardware and infrastructure modeling, (4) dynamic electricity and cloud GPU pricing simulation, and (5) predictive computation execution and performance evaluation. The framework supports multiple request arrival distributions, including stationary, nonstationary, bursty, periodic, and heavy-tailed workloads, together with variations in request complexity, semantic recurrence, prompt length, output length, and computational requirements. It further models heterogeneous GPU configurations, including differences in computational throughput, memory capacity, energy consumption, execution overhead, and cloud instance availability. Economic conditions are represented through configurable electricity price trajectories, time-dependent cloud GPU prices, resource allocation costs, and interruption-related expenditure. A discrete-event simulation architecture is formulated to coordinate request arrivals, prediction decisions, speculative computation, resource contention, cache operations, and online inference execution. A unified evaluation methodology is established to measure total computational expenditure, prediction effectiveness, GPU utilization, response latency, energy consumption, resource efficiency, and economic benefits across different experimental scenarios. The proposed environment is positioned as a reproducible experimental foundation for evaluating Predictive Computing for Generative AI (PCGA), Predictive Computational Orchestration for Generative AI (PCO-GAI), and Economic-Aware Predictive Computing for Generative AI (EAPC-GAI). Rather than assuming that predictive computation is universally advantageous, PCSE-GAI enables systematic investigation of the environmental conditions under which predictive preparation and economic-aware orchestration provide measurable benefits. Its simulation fidelity, generalizability, and practical usefulness remain subject to implementation and empirical validation.