TY - THES A1 - Tinnefeld, Christian T1 - Building a columnar database on shared main memory-based storage BT - database operator placement in a shared main memory-based storage system that supports data access and code execution N2 - In the field of disk-based parallel database management systems exists a great variety of solutions based on a shared-storage or a shared-nothing architecture. In contrast, main memory-based parallel database management systems are dominated solely by the shared-nothing approach as it preserves the in-memory performance advantage by processing data locally on each server. We argue that this unilateral development is going to cease due to the combination of the following three trends: a) Nowadays network technology features remote direct memory access (RDMA) and narrows the performance gap between accessing main memory inside a server and of a remote server to and even below a single order of magnitude. b) Modern storage systems scale gracefully, are elastic, and provide high-availability. c) A modern storage system such as Stanford's RAMCloud even keeps all data resident in main memory. Exploiting these characteristics in the context of a main-memory parallel database management system is desirable. The advent of RDMA-enabled network technology makes the creation of a parallel main memory DBMS based on a shared-storage approach feasible. This thesis describes building a columnar database on shared main memory-based storage. The thesis discusses the resulting architecture (Part I), the implications on query processing (Part II), and presents an evaluation of the resulting solution in terms of performance, high-availability, and elasticity (Part III). In our architecture, we use Stanford's RAMCloud as shared-storage, and the self-designed and developed in-memory AnalyticsDB as relational query processor on top. AnalyticsDB encapsulates data access and operator execution via an interface which allows seamless switching between local and remote main memory, while RAMCloud provides not only storage capacity, but also processing power. Combining both aspects allows pushing-down the execution of database operators into the storage system. We describe how the columnar data processed by AnalyticsDB is mapped to RAMCloud's key-value data model and how the performance advantages of columnar data storage can be preserved. The combination of fast network technology and the possibility to execute database operators in the storage system opens the discussion for site selection. We construct a system model that allows the estimation of operator execution costs in terms of network transfer, data processed in memory, and wall time. This can be used for database operators that work on one relation at a time - such as a scan or materialize operation - to discuss the site selection problem (data pull vs. operator push). Since a database query translates to the execution of several database operators, it is possible that the optimal site selection varies per operator. For the execution of a database operator that works on two (or more) relations at a time, such as a join, the system model is enriched by additional factors such as the chosen algorithm (e.g. Grace- vs. Distributed Block Nested Loop Join vs. Cyclo-Join), the data partitioning of the respective relations, and their overlapping as well as the allowed resource allocation. We present an evaluation on a cluster with 60 nodes where all nodes are connected via RDMA-enabled network equipment. We show that query processing performance is about 2.4x slower if everything is done via the data pull operator execution strategy (i.e. RAMCloud is being used only for data access) and about 27% slower if operator execution is also supported inside RAMCloud (in comparison to operating only on main memory inside a server without any network communication at all). The fast-crash recovery feature of RAMCloud can be leveraged to provide high-availability, e.g. a server crash during query execution only delays the query response for about one second. Our solution is elastic in a way that it can adapt to changing workloads a) within seconds, b) without interruption of the ongoing query processing, and c) without manual intervention. N2 - Diese Arbeit beschreibt die Erstellung einer spalten-orientierten Datenbank auf einem geteilten, Hauptspeicher-basierenden Speichersystem. Motiviert wird diese Arbeit durch drei Faktoren. Erstens ist moderne Netzwerktechnologie mit “Remote Direct Memory Access” (RDMA) ausgestattet. Dies reduziert den Unterschied hinsichtlich Latenz und Durchsatz zwischen dem Speicherzugriff innerhalb eines Rechners und auf einen entfernten Rechner auf eine Größenordnung. Zweitens skalieren moderne Speichersysteme, sind elastisch und hochverfügbar. Drittens hält ein modernes Speichersystem wie Stanford's RAMCloud alle Daten im Hauptspeicher vor. Diese Eigenschaften im Kontext einer spalten-orientierten Datenbank zu nutzen ist erstrebenswert. Die Arbeit ist in drei Teile untergliedert. Der erste Teile beschreibt die Architektur einer spalten-orientierten Datenbank auf einem geteilten, Hauptspeicher-basierenden Speichersystem. Hierbei werden die im Rahmen dieser Arbeit entworfene und entwickelte Datenbank AnalyticsDB sowie Stanford's RAMCloud verwendet. Die Architektur beschreibt wie Datenzugriff und Operatorausführung gekapselt werden um nahtlos zwischen lokalem und entfernten Hauptspeicher wechseln zu können. Weiterhin wird die Ablage der nach einem relationalen Schema formatierten Daten von AnalyticsDB in RAMCloud behandelt, welches mit einem Schlüssel-Wertpaar Datenmodell operiert. Der zweite Teil fokussiert auf die Implikationen bei der Abarbeitung von Datenbankanfragen. Hier steht die Diskussion im Vordergrund wo (entweder in AnalyticsDB oder in RAMCloud) und mit welcher Parametrisierung einzelne Datenbankoperationen ausgeführt werden. Dafür werden passende Kostenmodelle vorgestellt, welche die Abbildung von Datenbankoperationen ermöglichen, die auf einer oder mehreren Relationen arbeiten. Der dritte Teil der Arbeit präsentiert eine Evaluierung auf einem Verbund von 60 Rechnern hinsichtlich der Leistungsfähigkeit, der Hochverfügbarkeit und der Elastizität vom System. T2 - Die Erstellung einer spaltenorientierten Datenbank auf einem verteilten, Hauptspeicher-basierenden Speichersystem KW - computer science KW - database technology KW - main memory computing KW - cloud computing KW - verteilte Datenbanken KW - Hauptspeicher Technologie KW - virtualisierte IT-Infrastruktur Y1 - 2014 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:kobv:517-opus4-72063 ER - TY - GEN A1 - Wallenta, Daniel T1 - A Lefschetz fixed point formula for elliptic quasicomplexes T2 - Postprints der Universität Potsdam : Mathematisch Naturwissenschaftliche Reihe N2 - In a recent paper, the Lefschetz number for endomorphisms (modulo trace class operators) of sequences of trace class curvature was introduced. We show that this is a well defined, canonical extension of the classical Lefschetz number and establish the homotopy invariance of this number. Moreover, we apply the results to show that the Lefschetz fixed point formula holds for geometric quasiendomorphisms of elliptic quasicomplexes. T3 - Zweitveröffentlichungen der Universität Potsdam : Mathematisch-Naturwissenschaftliche Reihe - 885 KW - elliptic complexes KW - Fredholm complexes KW - Lefschetz number Y1 - 2020 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:kobv:517-opus4-435471 SN - 1866-8372 IS - 885 SP - 577 EP - 587 ER - TY - GEN A1 - Böckmann, Christine A1 - Osterloh, Lukas T1 - Runge-Kutta type regularization method for inversion of spheroidal particle distribution from limited optical data T2 - Postprints der Universität Potsdam : Mathematisch-Naturwissenschaftliche Reihe N2 - The Runge-Kutta type regularization method was recently proposed as a potent tool for the iterative solution of nonlinear ill-posed problems. In this paper we analyze the applicability of this regularization method for solving inverse problems arising in atmospheric remote sensing, particularly for the retrieval of spheroidal particle distribution. Our numerical simulations reveal that the Runge-Kutta type regularization method is able to retrieve two-dimensional particle distributions using optical backscatter and extinction coefficient profiles, as well as depolarization information. T3 - Zweitveröffentlichungen der Universität Potsdam : Mathematisch-Naturwissenschaftliche Reihe - 907 KW - inverse ill-posed problem KW - integral equation KW - laser remote sensing KW - inverse scattering KW - aerosol size distribution KW - 65R32 KW - 47A52 KW - 65R20 KW - 78A46 KW - iterative regularization Y1 - 2020 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:kobv:517-opus4-441200 SN - 1866-8372 IS - 907 SP - 150 EP - 165 ER - TY - GEN A1 - Hoos, Holger A1 - Lindauer, Marius A1 - Schaub, Torsten H. T1 - claspfolio 2 BT - advances in algorithm selection for answer set programming T2 - Postprints der Universität Potsdam : Mathematisch-Naturwissenschaftliche Reihe N2 - Building on the award-winning, portfolio-based ASP solver claspfolio, we present claspfolio 2, a modular and open solver architecture that integrates several different portfolio-based algorithm selection approaches and techniques. The claspfolio 2 solver framework supports various feature generators, solver selection approaches, solver portfolios, as well as solver-schedule-based pre-solving techniques. The default configuration of claspfolio 2 relies on a light-weight version of the ASP solver clasp to generate static and dynamic instance features. The flexible open design of claspfolio 2 is a distinguishing factor even beyond ASP. As such, it provides a unique framework for comparing and combining existing portfolio-based algorithm selection approaches and techniques in a single, unified framework. Taking advantage of this, we conducted an extensive experimental study to assess the impact of different feature sets, selection approaches and base solver portfolios. In addition to gaining substantial insights into the utility of the various approaches and techniques, we identified a default configuration of claspfolio 2 that achieves substantial performance gains not only over clasp's default configuration and the earlier version of claspfolio, but also over manually tuned configurations of clasp. T3 - Zweitveröffentlichungen der Universität Potsdam : Mathematisch-Naturwissenschaftliche Reihe - 606 KW - solver KW - sat Y1 - 2019 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:kobv:517-opus4-416129 SN - 1866-8372 IS - 606 ER -