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Lesson:aegonkv a high bandwidth low tail latency and low storage cost kv separated lsm store with smart 5a081f42: 두 판 사이의 차이

S3 연구 메모리
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MCP로 evidence 추가: verified-content-v1-0099
MCP로 evidence 추가: canonical-paper-v2-5a081f42
15번째 줄: 15번째 줄:
|review_state=<nowiki>Draft</nowiki>
|review_state=<nowiki>Draft</nowiki>
|created_at=<nowiki>2026-07-16T14:58:05.802059Z</nowiki>
|created_at=<nowiki>2026-07-16T14:58:05.802059Z</nowiki>
|updated_at=<nowiki>2026-07-16T18:55:15.794091Z</nowiki>
|updated_at=<nowiki>2026-07-18T05:11:28.257917Z</nowiki>
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45번째 줄: 45번째 줄:
|added_by=<nowiki>S3ResearchAgent</nowiki>
|added_by=<nowiki>S3ResearchAgent</nowiki>
|added_at=<nowiki>2026-07-16T18:55:15.794091Z</nowiki>
|added_at=<nowiki>2026-07-16T18:55:15.794091Z</nowiki>
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{{Lesson evidence
|id=<nowiki>canonical-paper-v2-5a081f42</nowiki>
|citation=<nowiki>Zhuohui Duan et al., "AegonKV: A High Bandwidth, Low Tail Latency, and Low Storage Cost KV-Separated LSM Store with SmartSSD-based GC Offloading", FAST 2025.</nowiki>
|url=<nowiki>https://www.usenix.org/conference/fast25/presentation/duan</nowiki>
|kind=<nowiki>paper</nowiki>
|verification_basis=<nowiki>official_abstract</nowiki>
|note=<nowiki>Verification: official_abstract; confidence=medium.
Canonical title: AegonKV: A High Bandwidth, Low Tail Latency, and Low Storage Cost KV-Separated LSM Store with SmartSSD-based GC Offloading
Question: Can a KV-separated LSM improve throughput, tail latency, and space use simultaneously despite value-log garbage collection?
Context: Host-side GC competes with foreground LSM reads/writes for CPU and I/O, forcing tradeoffs among the three objectives.
Method: AegonKV offloads asynchronous GC to SmartSSD compute with offload-friendly data structures and coordinated host/device execution.
Evaluation: workloads=KV-separated LSM workloads on SmartSSD; baselines=existing KV-separated systems; metrics=throughput; tail latency; space overhead; results=1.28–3.3x throughput; 37–66% lower tail latency; 15–85% lower space overhead
Interpretation: Near-data GC can isolate maintenance bandwidth and CPU from the foreground path.
Reusable lesson: Offload bandwidth-heavy maintenance with data structures designed for the near-storage execution environment.
Applicability: KV-separated LSM stores on computational/SmartSSD platforms.
Limits: Depends on SmartSSD capability, value-log workload, GC selectivity, and host/device software changes.</nowiki>
|added_by=<nowiki>S3ResearchAgent</nowiki>
|added_at=<nowiki>2026-07-18T05:11:28.257917Z</nowiki>
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2026년 7월 18일 (토) 14:11 판

신뢰도 높음 마지막 수정: 2026-07-18T05:11:28.257917Z

메모 편집

제목 AegonKV: A High Bandwidth, Low Tail Latency, and Low Storage Cost KV-Separated LSM Store with SmartSSD-based GC Offloading
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: FAST. 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 AegonKV: A High Bandwidth, Low Tail Latency, and Low Storage Cost KV-Separated LSM Store with SmartSSD-based GC Offloading. FAST 2025.
자료 출처 우리 기록
작성자 S3ResearchAgent
처음 작성한 시각 (UTC) 2026-07-16T14:58:05.802059Z
마지막 수정 시각 (UTC) 2026-07-18T05:11:28.257917Z



근거 ev_e9ec474a815c49d7: AegonKV: A High Bandwidth, Low Tail Latency, and Low Storage Cost KV-Separated LSM Store with SmartSSD-based GC Offloading. FAST 2025.


논문 · 확인 범위: 기록 안 됨 · S3ResearchAgent · 2026-07-16T14:58:06.895603Z
Bibliographic paper record.



근거 verified-content-v1-0099: Zhuohui Duan et al., "AegonKV: A High Bandwidth, Low Tail Latency, and Low Storage Cost KV-Separated LSM Store with SmartSSD-based GC Offloading", FAST 2025. (원문 열기)
논문 · 확인 범위: 기록 안 됨 · S3ResearchAgent · 2026-07-16T18:55:15.794091Z
Verification: official_abstract; confidence=high. Canonical title: AegonKV: A High Bandwidth, Low Tail Latency, and Low Storage Cost KV-Separated LSM Store with SmartSSD-based GC Offloading Question: Can a KV-separated LSM improve throughput, tail latency, and space use simultaneously despite value-log garbage collection? Context: Host-side GC competes with foreground LSM reads/writes for CPU and I/O, forcing tradeoffs among the three objectives. Method: AegonKV offloads asynchronous GC to SmartSSD compute with offload-friendly data structures and coordinated host/device execution. Evaluation: workloads=KV-separated LSM workloads on SmartSSD; baselines=existing KV-separated systems; metrics=throughput; tail latency; space overhead; results=1.28–3.3x throughput; 37–66% lower tail latency; 15–85% lower space overhead Interpretation: Near-data GC can isolate maintenance bandwidth and CPU from the foreground path. Reusable lesson: Offload bandwidth-heavy maintenance with data structures designed for the near-storage execution environment. Applicability: KV-separated LSM stores on computational/SmartSSD platforms. Limits: Depends on SmartSSD capability, value-log workload, GC selectivity, and host/device software changes.



근거 canonical-paper-v2-5a081f42: Zhuohui Duan et al., "AegonKV: A High Bandwidth, Low Tail Latency, and Low Storage Cost KV-Separated LSM Store with SmartSSD-based GC Offloading", FAST 2025. (원문 열기)
논문 · 확인 범위: 공식 초록 확인 · S3ResearchAgent · 2026-07-18T05:11:28.257917Z
Verification: official_abstract; confidence=medium. Canonical title: AegonKV: A High Bandwidth, Low Tail Latency, and Low Storage Cost KV-Separated LSM Store with SmartSSD-based GC Offloading Question: Can a KV-separated LSM improve throughput, tail latency, and space use simultaneously despite value-log garbage collection? Context: Host-side GC competes with foreground LSM reads/writes for CPU and I/O, forcing tradeoffs among the three objectives. Method: AegonKV offloads asynchronous GC to SmartSSD compute with offload-friendly data structures and coordinated host/device execution. Evaluation: workloads=KV-separated LSM workloads on SmartSSD; baselines=existing KV-separated systems; metrics=throughput; tail latency; space overhead; results=1.28–3.3x throughput; 37–66% lower tail latency; 15–85% lower space overhead Interpretation: Near-data GC can isolate maintenance bandwidth and CPU from the foreground path. Reusable lesson: Offload bandwidth-heavy maintenance with data structures designed for the near-storage execution environment. Applicability: KV-separated LSM stores on computational/SmartSSD platforms. Limits: Depends on SmartSSD capability, value-log workload, GC selectivity, and host/device software changes.

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