• Journal of KIISE:Computing Practices and Letters
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• v.14 no.2
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• pp.201-205
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• 2008

The flash memory is widely used as a main storage of embedded devices. It is adopted as a storage of database as growing the capacity of the flash memory. We run TPC-C benchmark on various FTL algorithms. But, the database shows poor performance on flash memory because the characteristic of I/O requests is full random. In this paper, we show the performance of all existing FTL algorithms is very poor. Especially, the FTL algorithm known as good at small mobile equipment shows worst performance. In addition, the chip-inter leaving which is a technique to improve the performance of the flash memory doesn't work well. In this paper, we inform you the reason that we need a new FTL algorithm and the direction for the database in the future.

• Journal of the Institute of Electronics and Information Engineers
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• v.50 no.8
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• pp.203-211
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• 2013

Due to the rapid growth of mobile devices and applications, mobile cloud computing is becoming an important platform in the development of cloud services. However, the mobile cloud computing is facing many challenges in terms of the computing resources and communications. One of them is the performance issue between mobile devices and cloud server. In the paper, we implemented a hadoop-based android mobile application, called mobile TPC-C, and used it for evaluating the performance aspect between mobile devices and cloud server. The mobile TPC-C was implemented based on the existing TPC-C, to make it possible to execute on top of android mobile devices. The performance measurement using mobile TPC-C was executed on various transactions while changing the number of mobile clients. By comparing it to the evaluation on the personal PC, we tried to point out the important aspects affecting the performance improvement between mobile clients and cloud server.

• Journal of Digital Convergence
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• v.7 no.3
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• pp.25-33
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• 2009

Historically, performance prediction and sizing of server systems have been the key purchasing argument for customer. To accurate server's sizing and performance prediction, it is necessary to correctness guideline for sizing and performance prediction. But existing guidelines have many errors. So, we examine the benchmarks of performance organization such as SPEC and TPC. And then we consider to TPC-C and TPC-E benchmarks for OLTP server's sizing and performance prediction that is a basic concept of guidelines. Eventually, we propose improvement of errors in guidelines.

• Proceedings of the Korea Society for Industrial Systems Conference
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• 1997.11a
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• pp.165-180
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• 1997

일반 병렬 처리 시스템(General Purpose Multiprocessors)과는 달리, 병렬 트랜잭션(Transaction) 처리 시스템의 성능은 메모리의 계층구조와 입출력 시스템의 구조 등에 크게 영향을 받는 특징을 갖는다. 본 논문은 입출력 노드의 성능 분석에 주안점을 두고 전제 시스템에서의 입출력 노드의 개수, 병렬 디스크의 개수 및 상호연결망(Interconnection Network)과의 접속을 제공하는 스위치의 처리 용량 등의 인수들이 SPAX 병렬 트랜잭션처리 시스템의 미치는 성능의 평가 및 분석에 대한 연구 내용 기술한다. 본 연구에서는 벤치마크로는 병렬 트랜잭션 시스템의 성능 평가에 주로 사용되고 있는 TPC-C 벤치마크를 사용하며 모의 입력(Synthetic workload)을 통한 성능분석을 수행하였다. 본 연구는 입출력노드에 부하가 많이 걸릴 경우 패킷의 크기에 따라 시스템의 성능에 큰 영향을 미치며, 반면에 입출력 노드내의 상호연결망의 접속(Interface)을 제공하는 XNIF의 데이터 버퍼 개수의 증가는 시스템의 성능 향상에 기여를 하지 않음을 보여준다. 이는 시스템의 성능향상을 위해서는 패킷 전송 경로상의 모든 시스템 요소의 성능 향상이 병행되어야 함을 보여준다. 마지막으로 프로세싱노드와 입출력노드의 처리능력의 균형이 병렬 트랜잭션 시스템의 설계에 있어서 매우 중요함을 보여준다.

• KIPS Transactions on Software and Data Engineering
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• v.4 no.9
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• pp.371-376
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• 2015

The Internet of Things (IoT) significantly increases the amount of data. Database compression is important for big data because it can reduce costs for storage systems and save I/O bandwidth. However, it could show low performance for write-intensive workloads such as OLTP due to the updates of compressed pages. In this paper, we present practical guidelines for the performance improvement of database compression. Especially, we propose the SELECTIVE strategy, which compresses only tables whose space savings are close to the expected space savings calculated by the compressed page size. Experimental results using the TPC-C benchmark and MySQL show that the strategy can achieve 1.1 times better performance than the uncompressed counterpart with 17.3% space savings.

• Proceedings of the Korean Information Science Society Conference
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• 2007.06a
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• pp.21-22
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• 2007

• The Journal of the Korea Contents Association
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• v.10 no.4
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• pp.84-90
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• 2010

DBMS is used for a range of applications from data warehousing through on-line transaction processing. As a result of this demand, DBMS has continued to grow in terms of its size. This growth invokes the most important issue of manually tuning the performance of DBMS. The DBMS tuning should be adaptive to the type of the workload put upon it. But, identifying workloads in mixed database applications might be quite difficult. Therefore, a method is necessary for identifying workloads in the mixed database environment. In this paper, we propose a SVM workload classifier to automatically identify a DBMS workload. Database workloads are collected in TPC-C and TPC-W benchmark while changing the resource parameters. Parameters for SVM workload classifier, C and kernel parameter, were chosen experimentally. The experiments revealed that the accuracy of the proposed SVM workload classifier is about 9% higher than that of Decision tree, Naive Bayes, Multilayer perceptron and K-NN classifier.

• Proceedings of the Korean Information Science Society Conference
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• 2005.11b
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• pp.112-114
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• 2005

본 논문은 SAN(storage area network)상에 네트워크-부착형 (network-attached) 저장 장치들을 직접 연결하여 파일 서버 없이 직접 데이터 전송이 가능한 SAN 기반의 클러스터 공유 파일 시스템인 $SANique^{TM}$의 설계 기법을 설명하고 $SANique^{TM}$ 시스템의 기본 파일 입$\cdot$출력 연산 성능을 집중적으로 평가하기 위해 상용 DBMS의 기본 연산 트랜잭션을 구성하여 기존 파일 시스템 기반의 DBMS와 비교 분석한다. 또한 표준 성능 평가 도구인 TPC-C 표준 벤치마크 테스트 도구를 활용하여 리눅스 시스템 및 Solaris 시스템 환경의 파일 시스템과 비교한다.

• The KIPS Transactions:PartD
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• v.11D no.7 s.96
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• pp.1427-1434
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• 2004

Database system benchmarks, which are usually evaluated to use the maximized resource in order to get the best results, arc not likely to simulate the real environment. We propose a workload generator that helps benchmarks be executed in the environment similar to a real world. The workload generator can create memory-bound, CPU-bound, and I/O-bound workloads. The workload generator allows users to create an integrated workload. which is similar to a real workload users run across in practice. Finally, we conducted the experiments that the Wisconsin benchmark was performed with the TPC-C and with the workload generation tool, and showed the feasibility of the proposed workload gen-eration tool comparing with two experimental results.

• Journal of KIISE:Computer Systems and Theory
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• v.34 no.9
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• pp.445-458
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• 2007

This article presents a novel infrastructure for large-memory database processing using commodity hardware with operating system support. We exploit inexpensive PCs and a high-speed network capable of Remote Direct Memory Access (RDMA) operations to build a new memory hierarchy between fast volatile memory and slow disk storage. The new memory hierarchy guarantees a reasonable response time, and its storage size enables us to run large-memory database systems with little performance degradation. The proposed architecture has two main components: (1) a remote memory system inside the Linux kernel to manage other computers' memory pages efficiently and (2) a remote memory pager responsible for manipulating remote read/write operations on remote memory pages. We insist that the proposed architecture is practical enough to support the rigorous demands of commercial in-memory database systems by demonstrating the performance of publicly available main-memory databases (e.g., MySQL) on our prototyped system. The experimental results show very interesting results from the TPC-C benchmark.