정형수 교수
Hyungsoo Jung
서울대학교 · 컴퓨터과학
연구실 소개
정형수 교수의 연구실은 고성능 컴퓨팅 환경에서의 스케일러블 시스템 설계와 성능 최적화를 핵심으로 삼고 있습니다. 특히 분산 데이터베이스 시스템의 동시성 제어, MapReduce 기반 대용량 데이터 처리 벤치마크, 고대역폭 지연 제품 네트워크에서의 혼잡 제어 프로토콜 등에서 핵심적인 기여를 하고 있습니다. 또한 클러스터 환경에서의 장애 내성 및 고가용성 기반 시스템 아키텍처 개발에도 주력하고 있습니다. 연구는 실제 시스템에서의 성능 문제를 진단하고, 이를 해결하기 위한 저수준 최적화와 혁신적 아키텍처 설계에 초점을 맞추고 있습니다.
연구 현황
연구 성과 추이
표시된 성과는 수집된 데이터 기준으로 산출되며, 일부 차이가 있을 수 있습니다.
주요 논문
15MapReduce is Google's programming model for easy development of scalable parallel applications which process huge quantity of data on many clusters. Due to its conveniency and efficiency, MapReduce is used in various applications (e.g., Web search services and online analytical processing). However, there are only few good benchmarks to evaluate MapReduce implementations by realistic testsets. In this paper, we present MRBench that is a benchmark for evaluating MapReduce systems. MRBench focuses
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Modern implementations of DBMS software are intended to take advantage of high core counts that are becoming common in high-end servers. However, we have observed that several database platforms, including MySQL, Shore-MT, and a commercial system, exhibit throughput collapse as load increases into oversaturation (where there are more request threads than cores), even for a workload with little or no logical contention for locks, such as a read-only workload. Our analysis of MySQL identifies latc
Modern databases, guaranteeing atomicity and durability, store transaction logs in a volatile, central log buffer and then flush the log buffer to non-volatile storage by the write-ahead logging principle. Buffering logs in central log store has recently faced a severe multicore scalability problem, and log flushing has been challenged by synchronous I/O delay. We have designed and implemented a fast and scalable logging method, E leda , that can migrate a surge of transaction logs from volatile
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