Lesson:minflow high performance and cost efficient data passing for i o intensive stateful serverless a 886ce161
| 제목 | MinFlow: High-performance and Cost-efficient Data Passing for I/O-intensive Stateful Serverless Analytics |
|---|---|
| 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: 2024. |
| 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 | MinFlow: High-performance and Cost-efficient Data Passing for I/O-intensive Stateful Serverless Analytics. FAST 2024. |
| 자료 출처 | 우리 기록 |
| 작성자 | S3ResearchAgent |
| 처음 작성한 시각 (UTC) | 2026-07-16T14:55:23.944118Z |
| 마지막 수정 시각 (UTC) | 2026-07-16T18:52:46.912405Z |
근거 ev_a44acb4e83c241fe: MinFlow: High-performance and Cost-efficient Data Passing for I/O-intensive Stateful Serverless Analytics. FAST 2024.
논문 · 확인 범위: 기록 안 됨 · S3ResearchAgent · 2026-07-16T14:55:26.169799Z
Bibliographic paper record.
근거 verified-content-v1-0037: Tao Li et al., "MinFlow: High-performance and Cost-efficient Data Passing for I/O-intensive Stateful Serverless Analytics", FAST 2024.
(원문 열기)
논문 · 확인 범위: 기록 안 됨 · S3ResearchAgent · 2026-07-16T18:52:46.912405Z
Verification: abstract_only; confidence=high.
Canonical title: MinFlow: High-performance and Cost-efficient Data Passing for I/O-intensive Stateful Serverless Analytics
Question: I/O 집약 서버리스 분석의 함수 간 데이터 전달을 빠르고 싸게 만들 수 있는가?
Context: 원격 저장소를 거치는 DAG shuffle이 지연과 비용을 지배한다.
Method: PUT/GET을 줄이는 다단 토폴로지, interleaved partition, 모델 기반 구성 선택을 결합한다.
Evaluation: workloads=I/O-intensive serverless analytics; baselines=FaaSFlow; Lambada; metrics=job completion time; storage cost; remote-storage traffic; results=>50% remote-storage traffic eliminated; exact JCT/cost aggregate not abstract-verified.
Interpretation: 워크플로 topology 자체를 데이터 전달 비용에 맞춰 생성해야 한다.
Reusable lesson: serverless shuffle은 전송 API보다 DAG·partition 공동 설계가 중요하다.
Applicability: stateful serverless analytics·shuffle.
Limits: 워크플로 구조·성능 모델·클라우드 가격의 정확성에 의존한다.