An Optimal Resource Allocation Scheme for Increasing RocksDB Parallelism on High-Performance Computing Systems

고성능 컴퓨팅 시스템은 높아진 컴퓨팅 파워와 고성능 저장장장치의 출현으로 점점 널리 사용되고 있는 추세이다. 고성능 컴퓨팅 시스템에서 동작하는 응용의 성능은 시스템의 가용 자원을 어떻게 할당하여 사용하느냐에 따라 좌우된다. 대표적인 소셜 네트워크 서비스 운영 기업인 페이스북에서는 자사의데이터 저장 및 관리를 위해 고성능 저장장치에 최적화 된 RocksDB라는 키-밸류 스토어를 사용하고 있다. RocksDB에서는 관리자가 시스템의 가용한 자원을 최적으로 할당할 수 있도록 다양한 설정을 제공한다. 본 논문에서는 고성능 컴퓨팅 시스템의 가용 자원을 할당하는 방법이 RocksDB의 성능에 미치는 영향을 분석한다. 이를 기반으로 RocksDB의 병렬성을 높여 성능을 향상시킬 수 있는 최적의 자원 할당 기법을 제안한다. High-performance computing systems are becoming more widely used because of the increased computing power and the emergence of high-performance storage devices. The performance of applications operating on the high-performance computing systems depends on how the systems’ available resources are allocated and applied. Facebook, a leading social network service company, uses a key-value store called RocksDB, optimized for high-performance storage devices for data storage and management. RocksDB provides a variety of configurations that allow administrators to optimally allocate available resources on their systems. In this paper, we analyzed the impact of the allocation methods of available resources of the high-performance computing systems on the performance of RocksDB. Based on this, we propose an optimal resource allocation technique that can improve the performance by increasing the parallelism of RocksDB.N

An Efficient File Distribution Scheme for Multiple Storage Devices based on I/O Characteristics of the Key-Value Store

최근 많은 데이터 센터들에서는 나날이 증가하는 데이터를 관리하기 위해 고성능 저장장치를 채택하고 있다. 또한, 다수의 장치를 사용하여 대용량 데이터의 효율적으로 처리한다. 그러나, 고성능 저장 장치를 사용함에도 불구하고 읽기와 쓰기 연산이 혼재되어 발생하는 일부 응용에서는 성능이 저하될 수 있다. 이는 장치 내부에서 쓰기 연산의 처리가 완료될 때까지 읽기 연산의 처리가 지연되기 때문이다. 본 논문에서는 응용에서 생성하는 파일들의 I/O 특성을 고려하여 다수의 장치에 효율적으로 분리 저장하는 기법을 제안한다. 실험 결과를 통해 읽기와 쓰기 연산을 분리하여 처리함으로써 응용의 성능이 최대 21% 향상됨을 보인다. Recently, many data centers have adopted high-performance storage devices to manage the ever-increasing data. In addition, multiple devices are used to efficiently process large amounts of data. However, despite the use of high-performance storage devices, performance can still be degraded when read and write operations are mixed in the workload. This is because the handling read operation is delayed until the write operations are completed. Thus, in this paper, we propose an efficient file distribution scheme based on I/O characteristics. The experimental results show that the performance of the application is improved by up to 21% by separating the read and write operations.N

Design and implementation of an efficient flushing scheme for cloud key-value storage

A key-value store is an essential component that is increasingly demanded in many scale-out environments, including social networks, online retail environments, and cloud services. Modern key-value storage engines provide many features, including transaction, versioning, and replication. In storage engines, transaction processing provides atomicity and durability by using write-ahead logging (WAL), which flushes log data before the data page is written to persistent storage in synchronous commit. However, according to our observation, WAL is a performance bottleneck in key-value storage engines since the flushing of log data to persistent storage incurs a significant overhead of lock contention and fsync() calls, even with the various optimizations in the existing scheme. In this article, we propose an approach to improve the performance of key-value storage by optimizing the existing flushing scheme combined with group commit and consolidate array. Our scheme aggregates the multiple flushing of log data into a large request on the fly and completes the request early. This scheme is an efficient group commit that reduces the number of frequent lock acquisitions and fsync() calls in the synchronous commit while supporting the same transaction level that the existing scheme provides. Furthermore, we integrate our flushing scheme into the replication system and evaluate it by using multiple nodes. We implement our scheme on the WiredTiger storage engine. The experimental results show that our scheme improves the performance of the key-value workload compared to the existing scheme.OAIID:RECH_ACHV_DSTSH_NO:T201806126RECH_ACHV_FG:RR00200001ADJUST_YN:EMP_ID:A002712CITE_RATE:1.601DEPT_NM:컴퓨터공학부EMAIL:[email protected]_YN:YN