Icarus: Towards a Multistore Database System

The last years have seen a vast diversification on the database market. In contrast to the "one-size-fits-all" paradigm according to which systems have been designed in the past, today's database management systems (DBMSs) are tuned for particular workloads. This has led to DBMSs optimized for high performance, high throughput read/write workload in online transaction processing (OLTP) and systems optimized for complex analytical queries (OLAP). However, this approach reaches a limit when systems have to deal with mixed workloads that are neither pure OLAP nor pure OLTP workloads. In such cases, polystores are increasingly gaining popularity. Rather than supporting one single database paradigm and addressing one particular workload, polystores encompass several DBMSs that store data in different schemas and allow to route requests at a per-query-level to the most appropriate system. In this paper, we introduce the polystore Icarus. In our evaluation based on a workload that combines OLTP and OLAP elements, We show that Icarus is able to speed-up queries up to a factor of 3 by properly routing queries to the best underlying DBMS

A QUALIFIED GRADED ATTRIBUTE-BASED ENCRYPTION ACCESS REGULATE MODE FOR ROVING CLOUD COMPUTING

To build a new niche system to run large-scale disadvantaged RDFD sections. New database strategies to collect buttons related to the database. In this document, we describe the PPR, a capable and virtualized RDF data management system for the cloud. Unlike the previous map, RPCL performs a physical analysis of the sample and Sca information before the information and the wallet. This machine keeps the current good reputation by running in a sliding window that works, as well as related tasks and overlapping edges. The article refers to the machining of the article through the eligibility of the RFJ chart, which is based on the local grid, horizontal navigation and the distribution industry. Important Pay key is a necessary index in RFJ, uses the use of a linguistic tree to implement each URL or word, and assigns a certain number of important values. Consuming such data by using graphics demography using conventional minute cutting algorithms results in very small dividends and more quantity. Many RDF systems are available in the Hash division and in the selection, the project and the segments. The grid system was from the first system, to administer neutral RDF at the poorest scale. Within this document, we describe the construction of RPLL, your organization of basic data, together with Neurosis’s, we use the data to distribute and distribute data. We produce an RPGCL product, our products will be shown exclusively, depending on the working conditions of the workload, instead of the two main reasons

ANNOYED-RESIDENT CONTACT CONTROL(CATCC) MODEL FOR COMPUTER STANDARD SPECIFICATION AND VERIFICATION

A completely new system architecture to treat fine grain RDF sections in a wide range. New data recruitment strategies to participate in the identification of relevant data segments. In this document, we describe RpCl, a distributed data management system and RDF for this cloud. Unlike the previous approach, RpCl administers a physiological analysis of the state information and the schema before dividing the information. The device maintains a sliding window that tracks the current good reputation of the workload, as well as relevant statistics on the number of connections to be made and the limits of criminalization. The machine combines the future representation by summarizing the RDF, which contains a local horizontal division of the triangles in a distributed network structure in the network. One important thing is a vital indicator in RpCl that uses a lexical tree to parse incoming or literal URIs and assign a distinguished number key value. The implementation of such data using classical techniques or the division of the graph using simple traditional algorithms leads to extremely inefficient distributions, as well as to a greater number of connections. Many RDF systems are based on hash defragmentation, as well as distributions, distributions and distributed connections. The Grape Network system was one of the first systems to carry out this decentralized management of RDF. In this document, we describe the structure of RpCl, its basic data structure, as well as the new algorithms that we use to divide and distribute data. We produce an integral vision of RpCl that shows that our product is usually two sizes faster than modern systems in standard workloads

AN PROFICIENT AND SCALABLE ORGANIZATION OF RESOURCE DESCRIPTION FRAMEWORK DATA IN THE CLOUD COMPUTING

A unusual technique construction to serve exquisite RDF dissolutions in sizable. Novel data arrangement strategies to co-locate semantically associated bits of data. Within this report, we recount RpCl, a decent and expandable dispersed RDF data supervision technique yet perplex. Unlike soon approaches, RpCl runs a corporeal evaluation of both proof and dummy instruction fronting separationing the science. The machinery keeps a sliding-window w tracking the modern good position for the load, counting associated data nearby in spite of joins that necessary ultimate performed and also the convicting edges. The structure combines join along pruning via RDF linear representation portrayal having a locality- stationed, even dissolutioning from the triples correct into a grid like, shared ratio organization. The Important Thing Index is a basic indicant in RpCl it utilizes a lexicovisual representationical tree to inspect each elect URI or accurate and select it a weird product key quality. Sharding such data applying understated techniques or separationing the chart accepting conventional min-cut conclusion gravitate very sloppy shared operations and also to a larger than volume of joins. Many RDF arrangements depose hash-subdivideing farther on appropriated selections, projections, and joins. Grid-Vine technique was by the whole of the first techniques act this poor massive decentralized RDF supervision. Within this script, we recount the construction of RpCl, its fundamental data organizations, better the new method we use to segregation and donate data. We assemble an considerable skim RpCl display our commodity is usually two orders of magnitude quicker than condition-of-the-art arrangements on test tasks at hands

AN INTEGRAL PART AUDITABLE RIGHT TO MULTIPLE PRIVACY CABINET IN A POPULAR COMMERCIAL PLACE IN CLOUD

A new system architecture for handling large-scale fine-grain RDF partitions. New data placement strategies to coordinate semantically related data. In this document we describe RpCl, a competent and scalable distributed RDF data management system for that cloud. Contrary to the previous approaches, RpCl performs a physiological analysis of the information of the institution and the program before dividing the information. The machine maintains a sliding window w that maintains the current good reputation of the workload, in addition to the related statistics on the number of connections that had to be made and also the incriminating ranges. The combinations of machines are combined with pruning by means of a graphic summary RDF, with a horizontal partition based on the location of the right of three tracks in a grid, as a dispersed index structure. The index of important assets is an important index in RpCl. Use a lexicographic tree to analyze each URI or incoming literal and assign a characteristic number of key values. The provision of such data using classical techniques or the division of the graph using traditional mining algorithms, leads to very inefficient dispersed operations and also to a greater number of connections. Many RDF systems depend on the hash partition, as well as the options, projections and distributed connections. The Grid-Vine system was one of the first systems to do this with poor decentralized RDF management on a large scale. In this document we describe the RpCl architecture, its main data structures, together with the new algorithms we use to divide and distribute data. We produce a comprehensive view of RpCl. Our product is usually two orders of magnitude faster than the latest systems in standard workloads

OctopusDB : flexible and scalable storage management for arbitrary database engines

We live in a dynamic age with the economy, the technology, and the people around us changing faster than ever before. Consequently, the data management needs in our modern world are much different than those envisioned by the early database inventors in the 70s. Today, enterprises face the challenge of managing ever-growing dataset sizes with dynamically changing query workloads. As a result, modern data managing systems, including relational as well as big data management systems, can no longer afford to be carved-in-stone solutions. Instead, data managing systems must inherently provide flexible data management techniques in order to cope with the constantly changing business needs. The current practice to deal with changing query workloads is to have a different specialized product for each workload type, e.g. row stores for OLTP workload, column stores for OLAP workload, streaming systems for streaming workload, and scan-oriented systems for shared query processing. However, this means that the enterprises have to now glue different data managing products together and copy data from one product to another, in order to support several query workloads. This has the additional penalty of managing a zoo of data managing systems in the first place, which is tedious, expensive, as well as counter-productive for modern enterprises. This thesis presents an alternative approach to supporting several query workloads in a data managing system. We observe that each specialized database product has a different data store, indicating that different query workloads work well with different data layouts. Therefore, a key requirement for supporting several query workloads is to support several data layouts. Therefore, in this thesis, we study ways to inject different data layouts into existing (and familiar) data managing systems. The goal is to develop a flexible storage layer which can support several query workloads in a single data managing system. We present a set of non-invasive techniques, coined Trojan Techniques, to inject different data layouts into a data managing system. The core idea of Trojan Techniques is to drop the assumption of having one fixed data store per data managing system. Trojan Techniques are non-invasive in the sense that they do not make heavy untenable changes to the system. Rather, they affect the data managing system from inside, almost at the core. As a result, Trojan Techniques bring significant improvements in query performance. It is interesting to note that in our approach we follow a design pattern that has been used in other non-invasive research works as well, such as PAX, fractal prefetching B+-trees, and RowCol. We propose four Trojan Techniques. First, Trojan Indexes add an additional index access path in Hadoop MapReduce. Second, Trojan Joins allow for co-partitioned joins in Hadoop MapReduce. Third, Trojan Layouts allow for row, column, or column-grouped layouts in Hadoop MapReduce. Together, these three techniques provide a highly flexible data storage layer for Hadoop MapReduce. Our final proposal, Trojan Columns, introduces columnar functionality in row-oriented relational databases, including closed source commercial databases, thus bridging the gap between row and column oriented databases. Our experimental results show that Trojan Techniques can improve the performance of Hadoop MapReduce by a factor of up to 18, and that of a top-notch commercial database product by a factor of up to 17.Wir leben in einer dynamischen Zeit, in der sich Wirtschaft, Technologie und Gesellschaft schneller verändern als jemals zuvor. Folglich unterscheiden sich die Anforderungen an Datenverarbeitung heute sehr von dem, was sich die Pioniere dieses Forschungsgebiets in den 70er Jahren ursprünglich ausgemalt hatten. Heutzutage sehen sich Firmen mit der Herausforderung konfrontiert, stark fluktuierende Anfragelasten über einer stetig wachsender Datenmengen zu bewältigen. Daher können es sich moderne Datenbanksysteme, sowohl relationale als auch Big Data Systeme, nicht mehr leisten, wie starre, in Stein gemeißelte Lösungen zu funktionieren. Stattdessen sollten moderne Datenbanksysteme von Grunde auf für flexible Datenverwaltung konzipiert werden, um mit sich ständig ändernden Anforderungen Schritt halten zu können. Die gegenwärtige Praxis im Umgang mit häufig wechselnden Anfragemustern besteht allerdings noch darin, jeweils unterschiedliche, spezialisierte Lösungen für die verschiedenen Anfragetypen zu nutzen - zum Beispiel zeilenorientierte Systeme für OLTP Anfragen, spaltenorientierte Systeme für OLAP Anfragen, Data Stream Management Systeme für kontinuierliche Datenströme und Scan-basierte Systeme für die Bearbeitung von vielen gleichzeitigen Anfragen. Leider setzt dieses Vorgehen aber voraus, dass die Unternehmen es schaffen die verschiedensten Systeme irgendwie miteinander zu verknüpfen und einen Datenaustausch zwischen ihnen zu gewährleisten. Ein zusätzlicher Nachteil ist, dass hierbei oft ein ganzes Sortiment von Datenbankprodukten eingerichtet und gepflegt werden muss, was sowohl zeit- als auch kostenintensiv und damit letztlich aufwendig ist. Diese Dissertation präsentiert eine alternative Lösung, um wechselnde Anfragemuster effizient mit einem einzigen Datenverwaltungssystem zu unterstützen. Aus der Beobachtung, dass jedes spezielle Datenbankprodukt unterschiedliche Ansätze zur Datenspeicherung nutzt, folgern wir, dass verschiedene Anfragen jeweils auf bestimmten Datenlayouts effizienter beantwortet werden können als auf anderen. Deshalb ist eine zentrale Anforderung zur effizienten Verarbeitung unterschiedlicher Anfragetypen mit nur einem System, dass dieses System verschiedene Datenlayouts unterstützen muss. Dazu untersuchen wir in dieser Arbeit Möglichkeiten, um verschiedene Datenlayouts nachträglich in bestehende (und bekannte) Datenbanksysteme einzuschleusen. Das Ziel hierbei ist die Entwicklung einer flexiblen Speicherschicht, die verschiedenste Anfragen in einem einzigen Datenbanksystem unterstützen kann. Wir haben hierzu eine Reihe von nichtinvasiven Techniken, auch Trojanische Techniken genannt, entwickelt, mit denen sich verschiedene Datenlayouts nachträglich in existierende Systeme einschleusen lassen. Die Grundidee hinter diesen Trojanischen Techniken ist es, die Annahme, dass jedes Datenbanksystem nur eine festgelegte Art der Datenspeicherung haben kann, fallen zu lassen. Die Trojanischen Techniken erfordern nur minimale Änderungen am ursprünglichen Datenbanksystem, sondern beeinflussen dessen Verhalten von innen heraus. Der Einsatz Trojanischen Techniken kann die Anfragegeschwindigkeit erheblich steigern. Wir folgen mit diesem Ansatz einem Entwurfsmuster, das auch in anderen nichtinvasiven Forschungsprojekten wie PAX, fpB+-Bäume und RowCol verwendet wurde. Wir stellen in dieser Arbeit vier verschiedene Trojanische Techniken vor. Als erstes zeigen wir, wie Trojanische Indexe die Integration eines Index in Hadoop MapReduce ermöglichen. Ergänzt wird dies durch Trojanische Joins, welche kopartitionierte Joins in Hadoop MapReduce ermöglichen. Danach zeigen wir, wie Trojanische Layouts Hadoop MapReduce um zeilen-, spalten- und gruppierte spaltenorientierte Datenlayouts erweitern. Zusammen bilden diese Techniken eine flexible Speicherschicht für das Hadoop MapReduce Framework. Unsere vierte Technik, Trojanische Spalten, erlaubt es uns, spaltenorientierte Datenverarbeitung nachträglich in zeilenbasierten Datenbanksysteme einzuführen und lässt sich sogar auf kommerzielle closed-source Produkten anwenden. Wir schließen damit die Lücke zwischen zeilen- und spaltenorientierten Datenbanksystemen. In unseren Experimenten zeigen wir, dass die Trojanischen Techniken die Leistung des Hadoop MapReduce Frameworks um das bis zu 18fache und die Geschwindigkeit einer aktuellen kommerziellen Datenbank um das 17fache erhöhen können

Energy-Aware Data Management on NUMA Architectures

The ever-increasing need for more computing and data processing power demands for a continuous and rapid growth of power-hungry data center capacities all over the world. As a first study in 2008 revealed, energy consumption of such data centers is becoming a critical problem, since their power consumption is about to double every 5 years. However, a recently (2016) released follow-up study points out that this threatening trend was dramatically throttled within the past years, due to the increased energy efficiency actions taken by data center operators. Furthermore, the authors of the study emphasize that making and keeping data centers energy-efficient is a continuous task, because more and more computing power is demanded from the same or an even lower energy budget, and that this threatening energy consumption trend will resume as soon as energy efficiency research efforts and its market adoption are reduced. An important class of applications running in data centers are data management systems, which are a fundamental component of nearly every application stack. While those systems were traditionally designed as disk-based databases that are optimized for keeping disk accesses as low a possible, modern state-of-the-art database systems are main memory-centric and store the entire data pool in the main memory, which replaces the disk as main bottleneck. To scale up such in-memory database systems, non-uniform memory access (NUMA) hardware architectures are employed that face a decreased bandwidth and an increased latency when accessing remote memory compared to the local memory. In this thesis, we investigate energy awareness aspects of large scale-up NUMA systems in the context of in-memory data management systems. To do so, we pick up the idea of a fine-grained data-oriented architecture and improve the concept in a way that it keeps pace with increased absolute performance numbers of a pure in-memory DBMS and scales up on NUMA systems in the large scale. To achieve this goal, we design and build ERIS, the first scale-up in-memory data management system that is designed from scratch to implement a data-oriented architecture. With the help of the ERIS platform, we explore our novel core concept for energy awareness, which is Energy Awareness by Adaptivity. The concept describes that software and especially database systems have to quickly respond to environmental changes (i.e., workload changes) by adapting themselves to enter a state of low energy consumption. We present the hierarchically organized Energy-Control Loop (ECL), which is a reactive control loop and provides two concrete implementations of our Energy Awareness by Adaptivity concept, namely the hardware-centric Resource Adaptivity and the software-centric Storage Adaptivity. Finally, we will give an exhaustive evaluation regarding the scalability of ERIS as well as our adaptivity facilities