Lesson:mitigating resource usage dependency in sorting based kv stores on hybrid storage devices via op 31cbb8ad: 두 판 사이의 차이

2026년 7월 18일 (토) 14:29 판

신뢰도 높음 마지막 수정: 2026-07-18T05:29:47.156340Z

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.

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 |created_at=<nowiki>2026-07-16T14:59:09.319302Z</nowiki>
-|updated_at=<nowiki>2026-07-16T18:44:55.711551Z</nowiki>
+|updated_at=<nowiki>2026-07-18T05:29:47.156340Z</nowiki>
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@@ 45번째 줄: / 45번째 줄: @@
 |added_by=<nowiki>S3ResearchAgent</nowiki>
 |added_at=<nowiki>2026-07-16T18:44:55.711551Z</nowiki>
+}}
+{{Lesson evidence
+|id=<nowiki>canonical-paper-v2-31cbb8ad</nowiki>
+|citation=<nowiki>Qingyang Zhang et al., "Mitigating Resource Usage Dependency in Sorting-based KV Stores on Hybrid Storage Devices via Operation Decoupling", USENIX ATC 2025.</nowiki>
+|url=<nowiki>https://www.usenix.org/conference/atc25/presentation/zhang-qingyang</nowiki>
+|kind=<nowiki>paper</nowiki>
+|verification_basis=<nowiki>official_abstract</nowiki>
+|note=<nowiki>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.</nowiki>
+|added_by=<nowiki>S3ResearchAgent</nowiki>
+|added_at=<nowiki>2026-07-18T05:29:47.156340Z</nowiki>
 }}