Technology Category
Planning, Routing, and Scheduling: Optimization of Spatio-temporal SystemsSearch and Optimization: Sampling/Simulation-based SearchData Mining & Knowledge Management: Representing, Reasoning, and Using Provenance, Trust
A Diffusion-Based Framework for Configurable and Realistic Multi-Storage Trace Generation
🤖 AI Summary
Existing storage trace generation methods suffer from low fidelity, poor configurability, and insufficient modeling of cross-device temporal dependencies. To address these limitations, this paper proposes DiTTO—the first diffusion-based framework for multi-device storage trace generation. DiTTO integrates time-series modeling with conditional guidance to support fine-grained user specifications (e.g., I/O access patterns, device load distributions) and explicitly captures inter-device temporal dependencies. Experimental evaluation demonstrates that DiTTO significantly outperforms state-of-the-art approaches: it achieves high fidelity (only 8% error), enhanced diversity (+32% Jensen–Shannon distance), and strong configuration consistency (>96% requirement matching rate). By generating highly realistic, customizable, and temporally coherent synthetic traces, DiTTO establishes a trustworthy foundation for storage system simulation, testing, and optimization.
Technology Category
Application Category
📝 Abstract
We propose DiTTO, a novel diffusion-based framework for generating realistic, precisely configurable, and diverse multi-device storage traces. Leveraging advanced diffusion tech- niques, 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.
Problem
Research questions and friction points this paper is trying to address.
Generates configurable multi-device storage traces
Captures temporal dynamics and inter-device dependencies
Ensures high fidelity with user-defined configurations
Innovation
Methods, ideas, or system contributions that make the work stand out.
Diffusion-based framework for multi-storage traces
Generates configurable traces with temporal dynamics
Achieves high fidelity with 8% configuration error
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