A Diffusion-Based Framework for Configurable and Realistic Multi-Storage Trace Generation
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Abstract (arXiv)
We propose DiTTO, a novel diffusion-based framework for generating realistic, precisely configurable, and diverse multi-device storage traces. Leveraging advanced diffusion techniques, DiTTO enables the synthesis of high-fidelity continuous traces that capture temporal dynamics and inter-device dependencies with user-defined configurations. Our experimental results demonstrate that DiTTO can generate traces with high fidelity and diversity while aligning closely with guided configurations with only 8% errors.
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References · 2
- 2021Learning Transferable Visual Models From Natural Language Supervisioncited by 33,744
- 2022Hierarchical Text-Conditional Image Generation with CLIP Latentscited by 6,585
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