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Limits: 결과는 트레이스와 캐시 크기에 의존하고 모든 워크로드에서 우세하지는 않다.</property:Applicability>
		<property:Attempt rdf:datatype="http://www.w3.org/2001/XMLSchema#string">S3-FIFO는 세 FIFO 큐와 빠른 강등으로 최근성·빈도·일회성 객체를 분리한다.</property:Attempt>
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		<property:Context rdf:datatype="http://www.w3.org/2001/XMLSchema#string">Venue: SOSP. Year: 2023.

LRU 계열은 동시성·메타데이터 비용이 크고 스캔 오염에 약하다.

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Source: https://doi.org/10.1145/3600006.3613147
Verification basis: official_abstract.
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		<property:Evidence_note rdf:datatype="http://www.w3.org/2001/XMLSchema#string">Verification: abstract_only; confidence=high.
Canonical title: FIFO Queues Are All You Need for Cache Eviction
Question: 복잡한 캐시 축출 알고리즘 없이도 높은 hit ratio와 확장성을 얻을 수 있는가?
Context: LRU 계열은 동시성·메타데이터 비용이 크고 스캔 오염에 약하다.
Method: S3-FIFO는 세 FIFO 큐와 빠른 강등으로 최근성·빈도·일회성 객체를 분리한다.
Evaluation: workloads=6,594 traces; 14 datasets; baselines=optimized LRU; state-of-the-art eviction algorithms; metrics=miss ratio; throughput; results=Best mean miss ratio on 10/14 datasets; 6× throughput vs 16-thread LRU.
Interpretation: 간단한 입장·퇴장 규칙만으로도 복잡한 전역 순서 유지 비용을 피할 수 있다.
Reusable lesson: 캐시 정책은 miss ratio와 동시성 비용을 함께 최적화하라.
Applicability: 웹·블록·객체 캐시.
Limits: 결과는 트레이스와 캐시 크기에 의존하고 모든 워크로드에서 우세하지는 않다.</property:Evidence_note>
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Canonical title: FIFO queues are all you need for cache eviction
Question: 복잡한 캐시 축출 알고리즘 없이도 높은 hit ratio와 확장성을 얻을 수 있는가?
Context: LRU 계열은 동시성·메타데이터 비용이 크고 스캔 오염에 약하다.
Method: S3-FIFO는 세 FIFO 큐와 빠른 강등으로 최근성·빈도·일회성 객체를 분리한다.
Evaluation: workloads=6,594 traces; 14 datasets; baselines=optimized LRU; state-of-the-art eviction algorithms; metrics=miss ratio; throughput; results=Best mean miss ratio on 10/14 datasets; 6× throughput vs 16-thread LRU.
Interpretation: 간단한 입장·퇴장 규칙만으로도 복잡한 전역 순서 유지 비용을 피할 수 있다.
Reusable lesson: 캐시 정책은 miss ratio와 동시성 비용을 함께 최적화하라.
Applicability: 웹·블록·객체 캐시.
Limits: 결과는 트레이스와 캐시 크기에 의존하고 모든 워크로드에서 우세하지는 않다.
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