The management of cloud resources for dynamic workloads presents a significant challenge. Learning-driven methods, including deep reinforcement learning, have emerged as promising solutions but necessitate extensive datasets for model training. Due to the practical constraints of acquiring real-world data, simulators are commonly employed to generate the requisite training workloads. However, a critical bottleneck arises from the architectural disconnect between CPU-bound simulation environments and GPU-accelerated training processes, leading to substantial data transfer overhead. To address this inefficiency, we propose a novel GPU-accelerated simulator implemented in the Julia programming language. Our framework is designed to unify simulation, model training, and inference, allowing these phases to execute almost entirely on GPUs. This approach effectively eliminates the traditional CPU-GPU communication bottleneck. Experimental evaluations demonstrate substantial performance gains over existing simulators, achieving up to a 96
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