WEB OF SCIENCE
SCOPUS
| DC Field | Value | Language |
|---|---|---|
| dc.contributor.advisor | 김예성 | - |
| dc.contributor.author | Seohyun Kim | - |
| dc.date.accessioned | 2026-01-23T10:57:01Z | - |
| dc.date.available | 2026-01-23T10:57:01Z | - |
| dc.date.issued | 2026 | - |
| dc.identifier.uri | https://scholar.dgist.ac.kr/handle/20.500.11750/59723 | - |
| dc.identifier.uri | http://dgist.dcollection.net/common/orgView/200000942224 | - |
| dc.description | Diffusion Models, Storage Systems, Block I/O Traces, Synthetic Data, System Benchmark | - |
| dc.description.tableofcontents | Ⅰ. Introduction 1 ⅠI. Background and Motivation 4 2.1 Lack of Real-world Traces 4 2.2 Limitations of Traditional Trace Generation Tools 5 2.3 Feasibility of Probabilistic Modeling 6 2.4 Limitations of Configuration-Based Simulation 8 2.5 Generative Models for Trace Generation 9 IⅠI. Overview of DiTTO 11 IV. Overview of STAMP 13 V. Detailed Method of STAMP 15 5.1 Data Pre-processing 15 5.1.1 Hierarchical Clustered Address Encoding 15 5.1.2 Uniform Feature Embedding 16 5.1.3 Hyper-configuration Labeling 17 5.2 Latent Representation Learning 18 5.3 CHIP : Contrastive Hyper-configuration and I/O trace Pre-training 22 5.4 Configuration-Guided Latent Denoising with U-Net 24 5.5 Inference 26 VI. Evaluation 28 6.1 Experimental Setup 28 6.2 Evaluation of DiTTO 29 6.3 Evaluation of STAMP 30 VII. Future Work 32 VIII. Conclusion 34 |
- |
| dc.format.extent | 38 | - |
| dc.language | eng | - |
| dc.publisher | DGIST | - |
| dc.title | A Diffusion-Based Framework for Configurable and Realistic Storage Trace Generation | - |
| dc.title.alternative | 구성 가능하고 현실적인 스토리지 트레이스 생성을 위한 확산 기반 프레임워크 | - |
| dc.type | Thesis | - |
| dc.identifier.doi | 10.22677/THESIS.200000942224 | - |
| dc.description.degree | Master | - |
| dc.contributor.department | Artificial Intelligence Major | - |
| dc.date.awarded | 2026-02-01 | - |
| dc.publisher.location | Daegu | - |
| dc.description.database | dCollection | - |
| dc.citation | XT.AM 김54 202602 | - |
| dc.date.accepted | 2026-01-19 | - |
| dc.contributor.alternativeDepartment | 학제학과인공지능전공 | - |
| dc.subject.keyword | Diffusion Models, Storage Systems, Block I/O Traces, Synthetic Data, System Benchmark | - |
| dc.contributor.affiliatedAuthor | Seohyun Kim | - |
| dc.contributor.affiliatedAuthor | Yeseong Kim | - |
| dc.contributor.alternativeName | 김서현 | - |
| dc.contributor.alternativeName | Yeseong Kim | - |
| dc.rights.embargoReleaseDate | 2031-02-28 | - |
