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Auflistung nach Autor:in "Seybold, Daniel"

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  • Konferenzbeitrag
    Experiences from Building the Open Database Performance Ranking with benchANT
    (Softwaretechnik-Trends Band 43, Heft 1, 2023) Seybold, Daniel; Domaschka, Jörg
    Benchmarking is an important method to advance database management systems (DBMS) from the industry and research perspective. Ensuring transparent and reproducible results is a key requirement to ensure the acceptance and credibility of benchmarking. To advance the research towards transparent and reproducible benchmark data, we report on building an open DBMS performance ranking with 130 benchmark configurations and ensuring comparability, transparency and reproducibility. We derive the required data on cloud, resource, DBMS and benchmark level to enable transparency and reproducibility and demonstrate the generation of such data sets with benchANT. Building upon such data, we outline future research directions for DBMS performance modelling, DBMS auto-tuning and decision support.
  • Konferenzbeitrag
    Understanding the Performance of Distributed Database Management Systems in Volatile Environments
    (Softwaretechnik-Trends Band 39, Heft 4, 2019) Domaschka, Jörg; Seybold, Daniel
    Cloud computing provides scalability and elasticity mechanisms on resource level and has become the preferred operational model for many applications. These, in turn, are based on distributed architectures trusting that this leads to scalability and elasticity and hence, good performance. Many applications rely on one or multiple database management systems (DBMS) as storage backends in order to manage their persistent state. Hence, the selection of a DBMS for a specific use case is crucial for performance and other non-functional properties. Yet, the choice is cumbersome due to the large number of available systems and the many impact factors ranging from the size of virtual resources, the type of the DBMS, and its architecture and scaling factor. In this paper, we summarise our experiences with performance evaluation for cloud-hosted DBMS in order to find well-suited configurations for specific use cases. We demonstrate that the overall performance of a distributed DBMS depends on three major domains (workload, cloud environment, and DBMS) with various parameters for each dimension.