Extending DBMSs with satellite databases

In this paper, we propose an extensible architecture for database engines where satellite databases are used to scale out and implement additional functionality for a centralized database engine. The architecture uses a middleware layer that offers consistent views and a single system image over a cluster of machines with database engines. One of these engines acts as a master copy while the others are read-only snapshots which we call satellites. The satellites are lightweight DBMSs used for scalability and to provide functionality difficult or expensive to implement in the main engine. Our approach also supports the dynamic creation of satellites to be able to autonomously adapt to varying loads. The paper presents the architecture, discusses the research problems it raises, and validates its feasibility with extensive experimental result

Adaptive Data Storage and Placement in Distributed Database Systems

Distributed database systems are widely used to provide scalable storage, update and query facilities for application data. Distributed databases primarily use data replication and data partitioning to spread load across nodes or sites. The presence of hotspots in workloads, however, can result in imbalanced load on the distributed system resulting in performance degradation. Moreover, updates to partitioned and replicated data can require expensive distributed coordination to ensure that they are applied atomically and consistently. Additionally, data storage formats, such as row and columnar layouts, can significantly impact latencies of mixed transactional and analytical workloads. Consequently, how and where data is stored among the sites in a distributed database can significantly affect system performance, particularly if the workload is not known ahead of time. To address these concerns, this thesis proposes adaptive data placement and storage techniques for distributed database systems. This thesis demonstrates that the performance of distributed database systems can be improved by automatically adapting how and where data is stored by leveraging online workload information. A two-tiered architecture for adaptive distributed database systems is proposed that includes an adaptation advisor that decides at which site(s) and how transactions execute. The adaptation advisor makes these decisions based on submitted transactions. This design is used in three adaptive distributed database systems presented in this thesis: (i) DynaMast that efficiently transfers data mastership to guarantee single-site transactions while maintaining well-understood and established transactional semantics, (ii) MorphoSys that selectively and adaptively replicates, partitions and remasters data based on a learned cost model to improve transaction processing, and (iii) Proteus that uses learned workload models to predictively and adaptively change storage layouts to support both high transactional throughput and low latency analytical queries. Collectively, this thesis is a concrete step towards autonomous database systems that allow users to specify only the data to store and the queries to execute, leaving the system to judiciously choose the storage and execution mechanisms to deliver high performance

A software architecture for electro-mobility services: a milestone for sustainable remote vehicle capabilities

To face the tough competition, changing markets and technologies in automotive industry, automakers have to be highly innovative. In the previous decades, innovations were electronics and IT-driven, which increased exponentially the complexity of vehicle’s internal network. Furthermore, the growing expectations and preferences of customers oblige these manufacturers to adapt their business models and to also propose mobility-based services. One other hand, there is also an increasing pressure from regulators to significantly reduce the environmental footprint in transportation and mobility, down to zero in the foreseeable future. This dissertation investigates an architecture for communication and data exchange within a complex and heterogeneous ecosystem. This communication takes place between various third-party entities on one side, and between these entities and the infrastructure on the other. The proposed solution reduces considerably the complexity of vehicle communication and within the parties involved in the ODX life cycle. In such an heterogeneous environment, a particular attention is paid to the protection of confidential and private data. Confidential data here refers to the OEM’s know-how which is enclosed in vehicle projects. The data delivered by a car during a vehicle communication session might contain private data from customers. Our solution ensures that every entity of this ecosystem has access only to data it has the right to. We designed our solution to be non-technological-coupling so that it can be implemented in any platform to benefit from the best environment suited for each task. We also proposed a data model for vehicle projects, which improves query time during a vehicle diagnostic session. The scalability and the backwards compatibility were also taken into account during the design phase of our solution. We proposed the necessary algorithms and the workflow to perform an efficient vehicle diagnostic with considerably lower latency and substantially better complexity time and space than current solutions. To prove the practicality of our design, we presented a prototypical implementation of our design. Then, we analyzed the results of a series of tests we performed on several vehicle models and projects. We also evaluated the prototype against quality attributes in software engineering

Hyperscale Data Processing With Network-Centric Designs

Today’s largest data processing workloads are hosted in cloud data centers. Due to unprecedented data growth and the end of Moore’s Law, these workloads have ballooned to the hyperscale level, encompassing billions to trillions of data items and hundreds to thousands of machines per query. Enabling and expanding with these workloads are highly scalable data center networks that connect up to hundreds of thousands of networked servers. These massive scales fundamentally challenge the designs of both data processing systems and data center networks, and the classic layered designs are no longer sustainable. Rather than optimize these massive layers in silos, we build systems across them with principled network-centric designs. In current networks, we redesign data processing systems with network-awareness to minimize the cost of moving data in the network. In future networks, we propose new interfaces and services that the cloud infrastructure offers to applications and codesign data processing systems to achieve optimal query processing performance. To transform the network to future designs, we facilitate network innovation at scale. This dissertation presents a line of systems work that covers all three directions. It first discusses GraphRex, a network-aware system that combines classic database and systems techniques to push the performance of massive graph queries in current data centers. It then introduces data processing in disaggregated data centers, a promising new cloud proposal. It details TELEPORT, a compute pushdown feature that eliminates data processing performance bottlenecks in disaggregated data centers, and Redy, which provides high-performance caches using remote disaggregated memory. Finally, it presents MimicNet, a fine-grained simulation framework that evaluates network proposals at datacenter scale with machine learning approximation. These systems demonstrate that our ideas in network-centric designs achieve orders of magnitude higher efficiency compared to the state of the art at hyperscale

Prefetching techniques for client server object-oriented database systems

The performance of many object-oriented database applications suffers from the page fetch latency which is determined by the expense of disk access. In this work we suggest several prefetching techniques to avoid, or at least to reduce, page fetch latency. In practice no prediction technique is perfect and no prefetching technique can entirely eliminate delay due to page fetch latency. Therefore we are interested in the trade-off between the level of accuracy required for obtaining good results in terms of elapsed time reduction and the processing overhead needed to achieve this level of accuracy. If prefetching accuracy is high then the total elapsed time of an application can be reduced significantly otherwise if the prefetching accuracy is low, many incorrect pages are prefetched and the extra load on the client, network, server and disks decreases the whole system performance. Access pattern of object-oriented databases are often complex and usually hard to predict accurately. The ..

OPTIMIZING CLIENT-SERVER COMMUNICATION FOR REMOTE SPATIAL DATABASE ACCESS

Technological advances in recent years have opened ways for easier creation of spatial data. Every day, vast amounts of data are collected by both governmental institutions (e.g., USGS, NASA) and commercial entities (e.g., IKONOS). This process is driven by increased popularity and affordability across the whole spectrum of collection methods, ranging from personal GPS units to satellite systems. Many collection methods such as satellite systems produce data in raster format. Often, such raster data is analyzed by the researchers directly, while at other times such data is used to produce the final dataset in vector format. With the rapidly increasing supply of data, more applications for this data are being developed that are of interest to a wider consumer base. The increasing popularity of spatial data viewers and query tools with end users introduces a requirement for methods to allow these basic users to access this data for viewing and querying instantly and without much effort. In our work, we focus on providing remote access to vector-based spatial data, rather than raster data. We explore new ways of allowing visualization of both spatial and non-spatial data stored in a central server database on a simple client connected to this server by possibly a slow and unreliable connection. We considered usage scenarios where transferring the whole database for processing on the client was not feasible. This is due to the large volume of data stored on the server as well as a lack of computing power on the client and a slow link between the two. We focus on finding an optimal way of distributing work between the server, clients, and possibly other entities introduced into the model for query evaluation and data management. We address issues of scalability for clients that have only limited access to system resources (e.g., a Java applet). Methods to allow these clients to provide an interactive user interface, even for databases of arbitrary size, are also examined

Exploiting method semantics in client cache consistency protocols for object-oriented databases

PhD ThesisData-shipping systems are commonly used in client-server object-oriented databases. This is in- tended to utilise clients' resources and improve scalability by allowing clients to run transactions locally after fetching the required database items from the database server. A consequence of this is that a database item can be cached at more than one client. This therefore raises issues regarding client cache consistency and concurrency control. A number of client cache consistency protocols have been studied, and some approaches to concurrency control for object-oriented datahases have been proposed. Existing client consistency protocols, however, do not consider method semantics in concurrency control. This study proposes a client cache consistency protocol where method se- mantic can be exploited in concurrency control. It identifies issues regarding the use of method semantics for the protocol and investigates the performance using simulation. The performance re- sults show that this can result in performance gains when compared to existing protocols. The study also shows the potential benefits of asynchronous version of the protoco

Adaptive Caching of Distributed Components

Die Zugriffslokalität referenzierter Daten ist eine wichtige Eigenschaft verteilter Anwendungen. Lokales Zwischenspeichern abgefragter entfernter Daten (Caching) wird vielfach bei der Entwicklung solcher Anwendungen eingesetzt, um diese Eigenschaft auszunutzen. Anschliessende Zugriffe auf diese Daten können so beschleunigt werden, indem sie aus dem lokalen Zwischenspeicher bedient werden. Gegenwärtige Middleware-Architekturen bieten dem Anwendungsprogrammierer jedoch kaum Unterstützung für diesen nicht-funktionalen Aspekt. Die vorliegende Arbeit versucht deshalb, Caching als separaten, konfigurierbaren Middleware-Dienst auszulagern. Durch die Einbindung in den Softwareentwicklungsprozess wird die frühzeitige Modellierung und spätere Wiederverwendung caching-spezifischer Metadaten gewährleistet. Zur Laufzeit kann sich das entwickelte System außerdem bezüglich der Cachebarkeit von Daten adaptiv an geändertes Nutzungsverhalten anpassen.Locality of reference is an important property of distributed applications. Caching is typically employed during the development of such applications to exploit this property by locally storing queried data: Subsequent accesses can be accelerated by serving their results immediately form the local store. Current middleware architectures however hardly support this non-functional aspect. The thesis at hand thus tries outsource caching as a separate, configurable middleware service. Integration into the software development lifecycle provides for early capturing, modeling, and later reuse of cachingrelated metadata. At runtime, the implemented system can adapt to caching access characteristics with respect to data cacheability properties, thus healing misconfigurations and optimizing itself to an appropriate configuration. Speculative prefetching of data probably queried in the immediate future complements the presented approach

Disseminating Updates to Mobile Clients

In this paper, we address the problem of propagating data updates to alarge number of mobile clients. Typically, mobile clients operateautonomously, i.e., disconnected from data servers, for prolongedperiods of time relying on locally replicated corporate data (e.g.,database views, file systems). From time to time, they need to refreshtheir replicas with data changes registered at a central datarepository. We propose a hybrid approach for delivering these updatesto the clients. We use a broadcast channel to "cache on the air"fresh updates for as long as they are high on demand. At the sametime, any requests for older updates are individually serviced by theserver through a separate channel. The air-caching satisfies the bulkof clients' needs, increasing data throughput many-fold compared totraditional data delivery mechanisms. We describe a hierarchicalair-cache structure, and analyze the performance of broadcasting a logof committed updates. Based on that, we propose a technique thatdynamically modifies the contents and the structure of the air-cacheaccording to the number and the (dis)connection habits of the clients.Through extensive simulations, we demonstrate the adaptiveness,efficiency, and practicality of the proposed system even for verylarge client populations

On indexing highly dynamic multidimensional datasets for interactive analytics

Orientador : Prof. Dr. Luis Carlos Erpen de BonaTese (doutorado) - Universidade Federal do Paraná, Setor de Ciências Exatas, Programa de Pós-Graduação em Informática. Defesa: Curitiba, 15/04/2016Inclui referências : f. 77-91Área de concentração : Ciência da computaçãoResumo: Indexação de dados multidimensionais tem sido extensivamente pesquisada nas últimas décadas. Neste trabalho, um novo workload OLAP identificado no Facebook é apresentado, caracterizado por (a) alta dinamicidade e dimensionalidade, (b) escala e (c) interatividade e simplicidade de consultas, inadequado para os SGBDs OLAP e técnicas de indexação de dados multidimensionais atuais. Baseado nesse caso de uso, uma nova estratégia de indexação e organização de dados multidimensionais para SGBDs em memória chamada Granular Partitioning é proposta. Essa técnica extende a visão tradicional de partitionamento em banco de dados, particionando por intervalo todas as dimensões do conjunto de dados e formando pequenos blocos que armazenam dados de forma não coordenada e esparsa. Desta forma, é possível atingir altas taxas de ingestão de dados sem manter estrutura auxiliar alguma de indexação. Este trabalho também descreve como um SGBD OLAP capaz de suportar um modelo de dados composto por cubos, dimensões e métricas, além de operações como roll-ups, drill-downs e slice and dice (filtros) eficientes pode ser construído com base nessa nova técnica de organização de dados. Com objetivo de validar experimentalmente a técnica apresentada, este trabalho apresenta o Cubrick, um novo SGBD OLAP em memória distribuída e otimizada para a execução de consultas analíticas baseado em Granular Partitioning, escritas desde a primeira linha de código para este trabalho. Finalmente, os resultados de uma avaliação experimental extensiva contendo conjuntos de dados e consultas coletadas de projetos pilotos que utilizam Cubrick é apresentada; em seguida, é mostrado que a escala desejada pode ser alcançada caso os dados sejam organizados de acordo com o Granular Partitioning e o projeto seja focado em simplicidade, ingerindo milhões de registros por segundo continuamente de uxos de dados em tempo real, e concorrentemente executando consultas com latência inferior a 1 segundo.Abstrct: Indexing multidimensional data has been an active focus of research in the last few decades. In this work, we present a new type of OLAP workload found at Facebook and characterized by (a) high dynamicity and dimensionality, (b) scale and (c) interactivity and simplicity of queries, that is unsuited for most current OLAP DBMSs and multidimensional indexing techniques. To address this use case, we propose a novel multidimensional data organization and indexing strategy for in-memory DBMSs called Granular Partitioning. This technique extends the traditional view of database partitioning by range partitioning every dimension of the dataset and organizing the data within small containers in an unordered and sparse fashion, in such a way to provide high ingestion rates and indexed access through every dimension without maintaining any auxiliary data structures. We also describe how an OLAP DBMS able to support a multidimensional data model composed of cubes, dimensions and metrics and operations such as roll-up, drill-down as well as efficient slice and dice filtering) can be built on top of this new data organization technique. In order to experimentally validate the described technique we present Cubrick, a new in-memory distributed OLAP DBMS for interactive analytics based on Granular Partitioning we have written from the ground up at Facebook. Finally, we present results from a thorough experimental evaluation that leveraged datasets and queries collected from a few pilot Cubrick deployments. We show that by properly organizing the dataset according to Granular Partitioning and focusing the design on simplicity, we are able to achieve the target scale and store tens of terabytes of in-memory data, continuously ingest millions of records per second from realtime data streams and still execute sub-second queries