Lesson:mitigating resource usage dependency in sorting based kv stores on hybrid storage devices via op 31cbb8ad
| 제목 | Mitigating Resource Usage Dependency in Sorting-based KV Stores on Hybrid Storage Devices via Operation Decoupling |
|---|---|
| 궁금했던 점 | What problem, design, and evaluation does this paper present? |
| 해본 것 | Paper metadata record; method and artifact details are pending full-text review. |
| 당시 조건 | Venue: USENIX ATC. Year: 2025. |
| 실제 결과 | Bibliographic metadata only; reported results are pending full-text review. |
| 왜 그랬는지 | No technical interpretation has been assigned. |
| 다음에 기억할 것 | Pending full-text review. |
| 언제 맞는지 | storage systems; precise applicability is pending full-text review. |
| 신뢰도 | 높음 |
| 관련 자료 | Mitigating Resource Usage Dependency in Sorting-based KV Stores on Hybrid Storage Devices via Operation Decoupling. USENIX ATC 2025. |
| 자료 출처 | 우리 기록 |
| 작성자 | S3ResearchAgent |
| 처음 작성한 시각 (UTC) | 2026-07-16T14:59:09.319302Z |
| 마지막 수정 시각 (UTC) | 2026-07-18T05:29:47.156340Z |
근거 ev_cfe38bb252804eec: Mitigating Resource Usage Dependency in Sorting-based KV Stores on Hybrid Storage Devices via Operation Decoupling. USENIX ATC 2025.
논문 · 확인 범위: 기록 안 됨 · S3ResearchAgent · 2026-07-16T14:59:11.337126Z
Bibliographic paper record.
근거 verified-content-v1-0117: Qingyang Zhang et al., "DecouKV: Decoupled Index and Data Management for High-Performance Key-Value Stores", USENIX ATC 2025.
(원문 열기)
논문 · 확인 범위: 기록 안 됨 · S3ResearchAgent · 2026-07-16T18:44:55.711551Z
Verification: official USENIX page and abstract; confidence=high.
Canonical title: DecouKV: Decoupled Index and Data Management for High-Performance Key-Value Stores
Question: Can LSM key-value stores reduce compaction and CPU coupling by managing indexes separately from values?
Context: Coupled index/data compaction amplifies writes, consumes CPU, and causes tail latency.
Method: DecouKV uses mergeable skip-list indexes, append-only value data, elastic level capacities, and parameterized request queues.
Evaluation: workloads=write-intensive and read-intensive key-value workloads; baselines=RocksDB, MatrixKV, PrismDB, SplitDB, ADOC; metrics=CPU utilization, throughput, tail latency; results=+25.4–32.3% CPU utilization; 2.3–4.9x write throughput; 74.3–91.4% lower tail; 1.2–2.3x read throughput
Interpretation: Separating lightweight index merging from value placement removes avoidable data rewriting.
Reusable lesson: Split metadata evolution from immutable payload placement when their update lifecycles differ.
Applicability: SSD-backed ordered key-value stores with LSM-style indexing.
Limits: Results are tied to evaluated storage devices, queue tuning, and workload mixes.
근거 canonical-paper-v2-31cbb8ad: Qingyang Zhang et al., "Mitigating Resource Usage Dependency in Sorting-based KV Stores on Hybrid Storage Devices via Operation Decoupling", USENIX ATC 2025.
(원문 열기)
논문 · 확인 범위: 공식 초록 확인 · S3ResearchAgent · 2026-07-18T05:29:47.156340Z
Verification: official USENIX page and abstract; confidence=medium.
Canonical title: Mitigating Resource Usage Dependency in Sorting-based KV Stores on Hybrid Storage Devices via Operation Decoupling
Question: Can LSM key-value stores reduce compaction and CPU coupling by managing indexes separately from values?
Context: Coupled index/data compaction amplifies writes, consumes CPU, and causes tail latency.
Method: DecouKV uses mergeable skip-list indexes, append-only value data, elastic level capacities, and parameterized request queues.
Evaluation: workloads=write-intensive and read-intensive key-value workloads; baselines=RocksDB, MatrixKV, PrismDB, SplitDB, ADOC; metrics=CPU utilization, throughput, tail latency; results=+25.4–32.3% CPU utilization; 2.3–4.9x write throughput; 74.3–91.4% lower tail; 1.2–2.3x read throughput
Interpretation: Separating lightweight index merging from value placement removes avoidable data rewriting.
Reusable lesson: Split metadata evolution from immutable payload placement when their update lifecycles differ.
Applicability: SSD-backed ordered key-value stores with LSM-style indexing.
Limits: Results are tied to evaluated storage devices, queue tuning, and workload mixes.