@misc{MintelRamsauerLohmannetal., author = {Mintel, Mario and Ramsauer, Ralf and Lohmann, Daniel and Scherzinger, Stefanie and Mauerer, Wolfgang}, title = {Fork {\`a} la carte f{\"u}r In-Memory-Datenbanken}, series = {Fr{\"u}hjahrstreffen der Fachgruppen Betriebssysteme, Hamburg, 17. M{\"a}rz 2022}, journal = {Fr{\"u}hjahrstreffen der Fachgruppen Betriebssysteme, Hamburg, 17. M{\"a}rz 2022}, language = {de} } @inproceedings{MauererRamsauerLucasetal., author = {Mauerer, Wolfgang and Ramsauer, Ralf and Lucas, Edson R. F. and Scherzinger, Stefanie}, title = {Silentium! Run-Analyse-Eradicate the Noise out of the DB/OS Stack}, series = {Datenbanksysteme f{\"u}r Business, Technologie und Web (BTW 2021): 13.-17. September 2021, Dresden, Deutschland}, booktitle = {Datenbanksysteme f{\"u}r Business, Technologie und Web (BTW 2021): 13.-17. September 2021, Dresden, Deutschland}, publisher = {Gesellschaft f{\"u}r Informatik}, doi = {10.18420/btw2021-21}, pages = {397 -- 421}, abstract = {When multiple tenants compete for resources, database performance tends to suffer. Yet there are scenarios where guaranteed sub-millisecond latencies are crucial, such as in real-time data processing, IoT devices, or when operating in safety-critical environments. In this paper, we study how to make query latencies deterministic in the face of noise (whether caused by other tenants or unrelated operating system tasks). We perform controlled experiments with an in-memory database engine in a multi-tenant setting, where we successively eradicate noisy interference from within the system software stack, to the point where the engine runs close to bare-metal on the underlying hardware. We show that we can achieve query latencies comparable to the database engine running as the sole tenant, but without noticeably impacting the workload of competing tenants. We discuss these results in the context of ongoing efforts to build custom operating systems for database workloads, and point out that for certain use cases, the margin for improvement is rather narrow. In fact, for scenarios like ours, existing operating systems might just be good enough, provided that they are expertly configured. We then critically discuss these findings in the light of a broader family of database systems (e.g., including disk-based), and how to extend the approach of this paper accordingly. Low-latency databases; tail latency; real-time databases; bounded-time query processing; DB-OS co-engineering}, language = {de} } @inproceedings{FruthScherzingerMauereretal., author = {Fruth, Michael and Scherzinger, Stefanie and Mauerer, Wolfgang and Ramsauer, Ralf}, title = {Tell-Tale Tail Latencies: Pitfalls and Perils in Database Benchmarking}, series = {Performance evaluation and benchmarking, 13th TPC Technology Conference (TPCTC 2021): Copenhagen, Denmark, August 20, 2021, Revised Selected Papers}, booktitle = {Performance evaluation and benchmarking, 13th TPC Technology Conference (TPCTC 2021): Copenhagen, Denmark, August 20, 2021, Revised Selected Papers}, editor = {Nambiar, Raghunath and Poess, Meikel}, publisher = {Springer}, address = {Cham, Switzerland}, isbn = {9783030944377}, doi = {10.1007/978-3-030-94437-7_8}, pages = {119 -- 134}, abstract = {The performance of database systems is usually characterised by their average-case (i.e., throughput) behaviour in standardised or de-facto standard benchmarks like TPC-X or YCSB. While tails of the latency (i.e., response time) distribution receive considerably less attention, they have been identified as a threat to the overall system performance: In large-scale systems, even a fraction of requests delayed can build up into delays perceivable by end users. To eradicate large tail latencies from database systems, the ability to faithfully record them, and likewise pinpoint them to the root causes, is imminently required. In this paper, we address the challenge of measuring tail latencies using standard benchmarks, and identify subtle perils and pitfalls. In particular, we demonstrate how Java-based benchmarking approaches can substantially distort tail latency observations, and discuss how the discovery of such problems is inhibited by the common focus on throughput performance. We make a case for purposefully re-designing database benchmarking harnesses based on these observations to arrive at faithful characterisations of database performance from multiple important angles.}, language = {en} } @misc{WeberDendorferSuessetal., author = {Weber, Karsten and Dendorfer, Sebastian and S{\"u}ß, Franz and Kubowitsch, Simone and Schratzenstaller, Thomas and Haug, Sonja and Mohr, Christa and Kiesl, Hans and Drechsler, J{\"o}rg and Westner, Markus and Kobus, J{\"o}rn and Schubert, Martin J. W. and Zenger, Stefan and Pietsch, Alexander and Weiß, Josef and Hinterseer, Sebastian and Schieck, Roland and Scherzinger, Stefanie and Klettke, Meike and Ringlstetter, Andreas and St{\"o}rl, Uta and Bissyand{\´e}, Tegawend{\´e} F. and Seeburger, Achim and Schindler, Timo and Ramsauer, Ralf and Kiszka, Jan and K{\"o}lbl, Andreas and Lohmann, Daniel and Mauerer, Wolfgang and Maier, Johannes and Scorna, Ulrike and Palm, Christoph and Soska, Alexander and Mottok, J{\"u}rgen and Ellermeier, Andreas and V{\"o}gele, Daniel and Hierl, Stefan and Briem, Ulrich and Buschmann, Knut and Ehrlich, Ingo and Pongratz, Christian and Pielmeier, Benjamin and Tyroller, Quirin and Monkman, Gareth J. and Gut, Franz and Roth, Carina and Hausler, Peter and Bierl, Rudolf and Prommesberger, Christian and Ławrowski, Robert Damian and Langer, Christoph and Schreiner, Rupert and Huang, Yifeng and She, Juncong and Ottl, Andreas and Rieger, Walter and Kraml, Agnes and Poxleitner, Thomas and Hofer, Simon and Heisterkamp, Benjamin and Lerch, Maximilian and Sammer, Nike and Golde, Olivia and Wellnitz, Felix and Schmid, Sandra and Muntschick, Claudia and Kusterle, Wolfgang and Paric, Ivan and Br{\"u}ckl, Oliver and Haslbeck, Matthias and Schmidt, Ottfried and Schwanzer, Peter and Rabl, Hans-Peter and Sterner, Michael and Bauer, Franz and Steinmann, Sven and Eckert, Fabian and Hofrichter, Andreas}, title = {Forschungsbericht 2017}, editor = {Baier, Wolfgang}, address = {Regensburg}, organization = {Ostbayerische Technische Hochschule Regensburg}, isbn = {978-3-9818209-3-5}, doi = {10.35096/othr/pub-1383}, url = {http://nbn-resolving.de/urn:nbn:de:bvb:898-opus4-13835}, subject = {Forschung}, language = {de} }