DiTTO uses diffusion models trained on rasterized storage traces to generate configurable, realistic multi-device I/O workloads with under 8% error on user-specified properties.
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A Diffusion-Based Framework for Configurable and Realistic Multi-Storage Trace Generation
DiTTO uses diffusion models trained on rasterized storage traces to generate configurable, realistic multi-device I/O workloads with under 8% error on user-specified properties.