Core Technologies for Native XML Database Management Systems

This work investigates the core technologies required to build Database Management Systems (DBMSs) for large collections of XML documents. We call such systems XML Base Management Systems (XBMSs). We identify requirements, and analyze how they can be met using a conventional DBMS. Our conclusion is that an XML support layer on top of an existing conventional DBMS does not address the requirements for XBMSs. Hence, we built a Native XBMS, called Natix. Natix has been developed completely from scratch, incorporating optimizations for high-performance XML processing in those places where they are most effective

Practical database replication

Tese de doutoramento em InformáticaSoftware-based replication is a cost-effective approach for fault-tolerance when combined with commodity hardware. In particular, shared-nothing database clusters built upon commodity machines and synchronized through eager software-based replication protocols have been driven by the distributed systems community in the last decade. The efforts on eager database replication, however, stem from the late 1970s with initial proposals designed by the database community. From that time, we have the distributed locking and atomic commitment protocols. Briefly speaking, before updating a data item, all copies are locked through a distributed lock, and upon commit, an atomic commitment protocol is responsible for guaranteeing that the transaction’s changes are written to a non-volatile storage at all replicas before committing it. Both these processes contributed to a poor performance. The distributed systems community improved these processes by reducing the number of interactions among replicas through the use of group communication and by relaxing the durability requirements imposed by the atomic commitment protocol. The approach requires at most two interactions among replicas and disseminates updates without necessarily applying them before committing a transaction. This relies on a high number of machines to reduce the likelihood of failures and ensure data resilience. Clearly, the availability of commodity machines and their increasing processing power makes this feasible. Proving the feasibility of this approach requires us to build several prototypes and evaluate them with different workloads and scenarios. Although simulation environments are a good starting point, mainly those that allow us to combine real (e.g., replication protocols, group communication) and simulated-code (e.g., database, network), full-fledged implementations should be developed and tested. Unfortunately, database vendors usually do not provide native support for the development of third-party replication protocols, thus forcing protocol developers to either change the database engines, when the source code is available, or construct in the middleware server wrappers that intercept client requests otherwise. The former solution is hard to maintain as new database releases are constantly being produced, whereas the latter represents a strenuous development effort as it requires us to rebuild several database features at the middleware. Unfortunately, the group-based replication protocols, optimistic or conservative, that had been proposed so far have drawbacks that present a major hurdle to their practicability. The optimistic protocols make it difficult to commit transactions in the presence of hot-spots, whereas the conservative protocols have a poor performance due to concurrency issues. In this thesis, we propose using a generic architecture and programming interface, titled GAPI, to facilitate the development of different replication strategies. The idea consists of providing key extensions to multiple DBMSs (Database Management Systems), thus enabling a replication strategy to be developed once and tested on several databases that have such extensions, i.e., those that are replication-friendly. To tackle the aforementioned problems in groupbased replication protocols, we propose using a novel protocol, titled AKARA. AKARA guarantees fairness, and thus all transactions have a chance to commit, and ensures great performance while exploiting parallelism as provided by local database engines. Finally, we outline a simple but comprehensive set of components to build group-based replication protocols and discuss key points in its design and implementation.A replicação baseada em software é uma abordagem que fornece um bom custo benefício para tolerância a falhas quando combinada com hardware commodity. Em particular, os clusters de base de dados “shared-nothing” construídos com hardware commodity e sincronizados através de protocolos “eager” têm sido impulsionados pela comunidade de sistemas distribuídos na última década. Os primeiros esforços na utilização dos protocolos “eager”, decorrem da década de 70 do século XX com as propostas da comunidade de base de dados. Dessa época, temos os protocolos de bloqueio distribuído e de terminação atómica (i.e. “two-phase commit”). De forma sucinta, antes de actualizar um item de dados, todas as cópias são bloqueadas através de um protocolo de bloqueio distribuído e, no momento de efetivar uma transacção, um protocolo de terminação atómica é responsável por garantir que as alterações da transacção são gravadas em todas as réplicas num sistema de armazenamento não-volátil. No entanto, ambos os processos contribuem para um mau desempenho do sistema. A comunidade de sistemas distribuídos melhorou esses processos, reduzindo o número de interacções entre réplicas, através do uso da comunicação em grupo e minimizando a rigidez os requisitos de durabilidade impostos pelo protocolo de terminação atómica. Essa abordagem requer no máximo duas interacções entre as réplicas e dissemina actualizações sem necessariamente aplicá-las antes de efectivar uma transacção. Para funcionar, a solução depende de um elevado número de máquinas para reduzirem a probabilidade de falhas e garantir a resiliência de dados. Claramente, a disponibilidade de hardware commodity e o seu poder de processamento crescente tornam essa abordagem possível. Comprovar a viabilidade desta abordagem obriga-nos a construir vários protótipos e a avaliálos com diferentes cargas de trabalho e cenários. Embora os ambientes de simulação sejam um bom ponto de partida, principalmente aqueles que nos permitem combinar o código real (por exemplo, protocolos de replicação, a comunicação em grupo) e o simulado (por exemplo, base de dados, rede), implementações reais devem ser desenvolvidas e testadas. Infelizmente, os fornecedores de base de dados, geralmente, não possuem suporte nativo para o desenvolvimento de protocolos de replicação de terceiros, forçando os desenvolvedores de protocolo a mudar o motor de base de dados, quando o código fonte está disponível, ou a construir no middleware abordagens que interceptam as solicitações do cliente. A primeira solução é difícil de manter já que novas “releases” das bases de dados estão constantemente a serem produzidas, enquanto a segunda representa um desenvolvimento árduo, pois obriga-nos a reconstruir vários recursos de uma base de dados no middleware. Infelizmente, os protocolos de replicação baseados em comunicação em grupo, optimistas ou conservadores, que foram propostos até agora apresentam inconvenientes que são um grande obstáculo à sua utilização. Com os protocolos optimistas é difícil efectivar transacções na presença de “hot-spots”, enquanto que os protocolos conservadores têm um fraco desempenho devido a problemas de concorrência. Nesta tese, propomos utilizar uma arquitetura genérica e uma interface de programação, intitulada GAPI, para facilitar o desenvolvimento de diferentes estratégias de replicação. A ideia consiste em fornecer extensões chaves para múltiplos SGBDs (Database Management Systems), permitindo assim que uma estratégia de replicação possa ser desenvolvida uma única vez e testada em várias bases de dados que possuam tais extensões, ou seja, aquelas que são “replicationfriendly”. Para resolver os problemas acima referidos nos protocolos de replicação baseados em comunicação em grupo, propomos utilizar um novo protocolo, intitulado AKARA. AKARA garante a equidade, portanto, todas as operações têm uma oportunidade de serem efectivadas, e garante um excelente desempenho ao tirar partido do paralelismo fornecido pelos motores de base de dados. Finalmente, propomos um conjunto simples, mas abrangente de componentes para construir protocolos de replicação baseados em comunicação em grupo e discutimos pontoschave na sua concepção e implementação

BriskStream: Scaling Data Stream Processing on Shared-Memory Multicore Architectures

We introduce BriskStream, an in-memory data stream processing system (DSPSs) specifically designed for modern shared-memory multicore architectures. BriskStream's key contribution is an execution plan optimization paradigm, namely RLAS, which takes relative-location (i.e., NUMA distance) of each pair of producer-consumer operators into consideration. We propose a branch and bound based approach with three heuristics to resolve the resulting nontrivial optimization problem. The experimental evaluations demonstrate that BriskStream yields much higher throughput and better scalability than existing DSPSs on multi-core architectures when processing different types of workloads.Comment: To appear in SIGMOD'1

Novel database design for extreme scale corpus analysis

This thesis presents the patterns and methods uncovered in the development of a new scalable corpus database management system, LexiDB, which can handle the ever-growing size of modern corpus datasets. Initially, an exploration of existing corpus data systems is conducted which examines their usage in corpus linguistics as well as their underlying architectures. From this survey, it is identified that existing systems are designed primarily to be vertically scalable (i.e. scalable through the usage of bigger, better and faster hardware). This motivates a wider examination of modern distributable database management systems and information retrieval techniques used for indexing and retrieval. These techniques are modified and adapted into an architecture that can be horizontally scaled to handle ever bigger corpora. Based on this architecture several new methods for querying and retrieval that improve upon existing techniques are proposed as modern approaches to query extremely large annotated text collections for corpus analysis. The effectiveness of these techniques and the scalability of the architecture is evaluated where it is demonstrated that the architecture is comparably scalable to two modern No-SQL database management systems and outperforms existing corpus data systems in token level pattern querying whilst still supporting character level pattern matching

Effizienz in Cluster-Datenbanksystemen - Dynamische und Arbeitslastberücksichtigende Skalierung und Allokation

Database systems have been vital in all forms of data processing for a long time. In recent years, the amount of processed data has been growing dramatically, even in small projects. Nevertheless, database management systems tend to be static in terms of size and performance which makes scaling a difficult and expensive task. Because of performance and especially cost advantages more and more installed systems have a shared nothing cluster architecture. Due to the massive parallelism of the hardware programming paradigms from high performance computing are translated into data processing. Database research struggles to keep up with this trend. A key feature of traditional database systems is to provide transparent access to the stored data. This introduces data dependencies and increases system complexity and inter process communication. Therefore, many developers are exchanging this feature for a better scalability. However, explicitly managing the data distribution and data flow requires a deep understanding of the distributed system and reduces the possibilities for automatic and autonomic optimization. In this thesis we present an approach for database system scaling and allocation that features good scalability although it keeps the data distribution transparent. The first part of this thesis analyzes the challenges and opportunities for self-scaling database management systems in cluster environments. Scalability is a major concern of Internet based applications. Access peaks that overload the application are a financial risk. Therefore, systems are usually configured to be able to process peaks at any given moment. As a result, server systems often have a very low utilization. In distributed systems the efficiency can be increased by adapting the number of nodes to the current workload. We propose a processing model and an architecture that allows efficient self-scaling of cluster database systems. In the second part we consider different allocation approaches. To increase the efficiency we present a workload-aware, query-centric model. The approach is formalized; optimal and heuristic algorithms are presented. The algorithms optimize the data distribution for local query execution and balance the workload according to the query history. We present different query classification schemes for different forms of partitioning. The approach is evaluated for OLTP and OLAP style workloads. It is shown that variants of the approach scale well for both fields of application. The third part of the thesis considers benchmarks for large, adaptive systems. First, we present a data generator for cloud-sized applications. Due to its architecture the data generator can easily be extended and configured. A key feature is the high degree of parallelism that makes linear speedup for arbitrary numbers of nodes possible. To simulate systems with user interaction, we have analyzed a productive online e-learning management system. Based on our findings, we present a model for workload generation that considers the temporal dependency of user interaction.Datenbanksysteme sind seit langem die Grundlage für alle Arten von Informationsverarbeitung. In den letzten Jahren ist das Datenaufkommen selbst in kleinen Projekten dramatisch angestiegen. Dennoch sind viele Datenbanksysteme statisch in Bezug auf ihre Kapazität und Verarbeitungsgeschwindigkeit was die Skalierung aufwendig und teuer macht. Aufgrund der guten Geschwindigkeit und vor allem aus Kostengründen haben immer mehr Systeme eine Shared-Nothing-Architektur, bestehen also aus unabhängigen, lose gekoppelten Rechnerknoten. Da dieses Konstruktionsprinzip einen sehr hohen Grad an Parallelität aufweist, werden zunehmend Programmierparadigmen aus dem klassischen Hochleistungsrechen für die Informationsverarbeitung eingesetzt. Dieser Trend stellt die Datenbankforschung vor große Herausforderungen. Eine der grundlegenden Eigenschaften traditioneller Datenbanksysteme ist der transparente Zugriff zu den gespeicherten Daten, der es dem Nutzer erlaubt unabhängig von der internen Organisation auf die Daten zuzugreifen. Die resultierende Unabhängigkeit führt zu Abhängigkeiten in den Daten und erhöht die Komplexität der Systeme und der Kommunikation zwischen einzelnen Prozessen. Daher wird Transparenz von vielen Entwicklern für eine bessere Skalierbarkeit geopfert. Diese Entscheidung führt dazu, dass der die Datenorganisation und der Datenfluss explizit behandelt werden muss, was die Möglichkeiten für eine automatische und autonome Optimierung des Systems einschränkt. Der in dieser Arbeit vorgestellte Ansatz zur Skalierung und Allokation erhält den transparenten Zugriff und zeichnet sich dabei durch seine vollständige Automatisierbarkeit und sehr gute Skalierbarkeit aus. Im ersten Teil dieser Dissertation werden die Herausforderungen und Chancen für selbst-skalierende Datenbankmanagementsysteme behandelt, die in auf Computerclustern betrieben werden. Gute Skalierbarkeit ist eine notwendige Eigenschaft für Anwendungen, die über das Internet zugreifbar sind. Lastspitzen im Zugriff, die die Anwendung überladen stellen ein finanzielles Risiko dar. Deshalb werden Systeme so konfiguriert, dass sie eventuelle Lastspitzen zu jedem Zeitpunkt verarbeiten können. Das führt meist zu einer im Schnitt sehr geringen Auslastung der unterliegenden Systeme. Eine Möglichkeit dieser Ineffizienz entgegen zu steuern ist es die Anzahl der verwendeten Rechnerknoten an die vorliegende Last anzupassen. In dieser Dissertation werden ein Modell und eine Architektur für die Anfrageverarbeitung vorgestellt, mit denen es möglich ist Datenbanksysteme auf Clusterrechnern einfach und effizient zu skalieren. Im zweiten Teil der Arbeit werden verschieden Möglichkeiten für die Datenverteilung behandelt. Um die Effizienz zu steigern wird ein Modell verwendet, das die Lastverteilung im Anfragestrom berücksichtigt. Der Ansatz ist formalisiert und optimale und heuristische Lösungen werden präsentiert. Die vorgestellten Algorithmen optimieren die Datenverteilung für eine lokale Ausführung aller Anfragen und balancieren die Last auf den Rechnerknoten. Es werden unterschiedliche Arten der Anfrageklassifizierung vorgestellt, die zu verschiedenen Arten von Partitionierung führen. Der Ansatz wird sowohl für Onlinetransaktionsverarbeitung, als auch Onlinedatenanalyse evaluiert. Die Evaluierung zeigt, dass der Ansatz für beide Felder sehr gut skaliert. Im letzten Teil der Arbeit werden verschiedene Techniken für die Leistungsmessung von großen, adaptiven Systemen präsentiert. Zunächst wird ein Datengenerierungsansatz gezeigt, der es ermöglicht sehr große Datenmengen völlig parallel zu erzeugen. Um die Benutzerinteraktion von Onlinesystemen zu simulieren wurde ein produktives E-learningsystem analysiert. Anhand der Analyse wurde ein Modell für die Generierung von Arbeitslasten erstellt, das die zeitlichen Abhängigkeiten von Benutzerinteraktion berücksichtigt

Modern data analytics in the cloud era

Cloud Computing ist die dominante Technologie des letzten Jahrzehnts. Die Benutzerfreundlichkeit der verwalteten Umgebung in Kombination mit einer nahezu unbegrenzten Menge an Ressourcen und einem nutzungsabhängigen Preismodell ermöglicht eine schnelle und kosteneffiziente Projektrealisierung für ein breites Nutzerspektrum. Cloud Computing verändert auch die Art und Weise wie Software entwickelt, bereitgestellt und genutzt wird. Diese Arbeit konzentriert sich auf Datenbanksysteme, die in der Cloud-Umgebung eingesetzt werden. Wir identifizieren drei Hauptinteraktionspunkte der Datenbank-Engine mit der Umgebung, die veränderte Anforderungen im Vergleich zu traditionellen On-Premise-Data-Warehouse-Lösungen aufweisen. Der erste Interaktionspunkt ist die Interaktion mit elastischen Ressourcen. Systeme in der Cloud sollten Elastizität unterstützen, um den Lastanforderungen zu entsprechen und dabei kosteneffizient zu sein. Wir stellen einen elastischen Skalierungsmechanismus für verteilte Datenbank-Engines vor, kombiniert mit einem Partitionsmanager, der einen Lastausgleich bietet und gleichzeitig die Neuzuweisung von Partitionen im Falle einer elastischen Skalierung minimiert. Darüber hinaus führen wir eine Strategie zum initialen Befüllen von Puffern ein, die es ermöglicht, skalierte Ressourcen unmittelbar nach der Skalierung auszunutzen. Cloudbasierte Systeme sind von fast überall aus zugänglich und verfügbar. Daten werden häufig von zahlreichen Endpunkten aus eingespeist, was sich von ETL-Pipelines in einer herkömmlichen Data-Warehouse-Lösung unterscheidet. Viele Benutzer verzichten auf die Definition von strikten Schemaanforderungen, um Transaktionsabbrüche aufgrund von Konflikten zu vermeiden oder um den Ladeprozess von Daten zu beschleunigen. Wir führen das Konzept der PatchIndexe ein, die die Definition von unscharfen Constraints ermöglichen. PatchIndexe verwalten Ausnahmen zu diesen Constraints, machen sie für die Optimierung und Ausführung von Anfragen nutzbar und bieten effiziente Unterstützung bei Datenaktualisierungen. Das Konzept kann auf beliebige Constraints angewendet werden und wir geben Beispiele für unscharfe Eindeutigkeits- und Sortierconstraints. Darüber hinaus zeigen wir, wie PatchIndexe genutzt werden können, um fortgeschrittene Constraints wie eine unscharfe Multi-Key-Partitionierung zu definieren, die eine robuste Anfrageperformance bei Workloads mit unterschiedlichen Partitionsanforderungen bietet. Der dritte Interaktionspunkt ist die Nutzerinteraktion. Datengetriebene Anwendungen haben sich in den letzten Jahren verändert. Neben den traditionellen SQL-Anfragen für Business Intelligence sind heute auch datenwissenschaftliche Anwendungen von großer Bedeutung. In diesen Fällen fungiert das Datenbanksystem oft nur als Datenlieferant, während der Rechenaufwand in dedizierten Data-Science- oder Machine-Learning-Umgebungen stattfindet. Wir verfolgen das Ziel, fortgeschrittene Analysen in Richtung der Datenbank-Engine zu verlagern und stellen das Grizzly-Framework als DataFrame-zu-SQL-Transpiler vor. Auf dieser Grundlage identifizieren wir benutzerdefinierte Funktionen (UDFs) und maschinelles Lernen (ML) als wichtige Aufgaben, die von einer tieferen Integration in die Datenbank-Engine profitieren würden. Daher untersuchen und bewerten wir Ansätze für die datenbankinterne Ausführung von Python-UDFs und datenbankinterne ML-Inferenz.Cloud computing has been the groundbreaking technology of the last decade. The ease-of-use of the managed environment in combination with nearly infinite amount of resources and a pay-per-use price model enables fast and cost-efficient project realization for a broad range of users. Cloud computing also changes the way software is designed, deployed and used. This thesis focuses on database systems deployed in the cloud environment. We identify three major interaction points of the database engine with the environment that show changed requirements compared to traditional on-premise data warehouse solutions. First, software is deployed on elastic resources. Consequently, systems should support elasticity in order to match workload requirements and be cost-effective. We present an elastic scaling mechanism for distributed database engines, combined with a partition manager that provides load balancing while minimizing partition reassignments in the case of elastic scaling. Furthermore we introduce a buffer pre-heating strategy that allows to mitigate a cold start after scaling and leads to an immediate performance benefit using scaling. Second, cloud based systems are accessible and available from nearly everywhere. Consequently, data is frequently ingested from numerous endpoints, which differs from bulk loads or ETL pipelines in a traditional data warehouse solution. Many users do not define database constraints in order to avoid transaction aborts due to conflicts or to speed up data ingestion. To mitigate this issue we introduce the concept of PatchIndexes, which allow the definition of approximate constraints. PatchIndexes maintain exceptions to constraints, make them usable in query optimization and execution and offer efficient update support. The concept can be applied to arbitrary constraints and we provide examples of approximate uniqueness and approximate sorting constraints. Moreover, we show how PatchIndexes can be exploited to define advanced constraints like an approximate multi-key partitioning, which offers robust query performance over workloads with different partition key requirements. Third, data-centric workloads changed over the last decade. Besides traditional SQL workloads for business intelligence, data science workloads are of significant importance nowadays. For these cases the database system might only act as data delivery, while the computational effort takes place in data science or machine learning (ML) environments. As this workflow has several drawbacks, we follow the goal of pushing advanced analytics towards the database engine and introduce the Grizzly framework as a DataFrame-to-SQL transpiler. Based on this we identify user-defined functions (UDFs) and machine learning inference as important tasks that would benefit from a deeper engine integration and investigate approaches to push these operations towards the database engine

Allocation Strategies for Data-Oriented Architectures

Data orientation is a common design principle in distributed data management systems. In contrast to process-oriented or transaction-oriented system designs, data-oriented architectures are based on data locality and function shipping. The tight coupling of data and processing thereon is implemented in different systems in a variety of application scenarios such as data analysis, database-as-a-service, and data management on multiprocessor systems. Data-oriented systems, i.e., systems that implement a data-oriented architecture, bundle data and operations together in tasks which are processed locally on the nodes of the distributed system. Allocation strategies, i.e., methods that decide the mapping from tasks to nodes, are core components in data-oriented systems. Good allocation strategies can lead to balanced systems while bad allocation strategies cause skew in the load and therefore suboptimal application performance and infrastructure utilization. Optimal allocation strategies are hard to find given the complexity of the systems, the complicated interactions of tasks, and the huge solution space. To ensure the scalability of data-oriented systems and to keep them manageable with hundreds of thousands of tasks, thousands of nodes, and dynamic workloads, fast and reliable allocation strategies are mandatory. In this thesis, we develop novel allocation strategies for data-oriented systems based on graph partitioning algorithms. Therefore, we show that systems from different application scenarios with different abstraction levels can be generalized to generic infrastructure and workload descriptions. We use weighted graph representations to model infrastructures with bounded and unbounded, i.e., overcommited, resources and possibly non-linear performance characteristics. Based on our generalized infrastructure and workload model, we formalize the allocation problem, which seeks valid and balanced allocations that minimize communication. Our allocation strategies partition the workload graph using solution heuristics that work with single and multiple vertex weights. Novel extensions to these solution heuristics can be used to balance penalized and secondary graph partition weights. These extensions enable the allocation strategies to handle infrastructures with non-linear performance behavior. On top of the basic algorithms, we propose methods to incorporate heterogeneous infrastructures and to react to changing workloads and infrastructures by incrementally updating the partitioning. We evaluate all components of our allocation strategy algorithms and show their applicability and scalability with synthetic workload graphs. In end-to-end--performance experiments in two actual data-oriented systems, a database-as-a-service system and a database management system for multiprocessor systems, we prove that our allocation strategies outperform alternative state-of-the-art methods

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

Sensor web geoprocessing on the grid

Recent standardisation initiatives in the fields of grid computing and geospatial sensor middleware provide an exciting opportunity for the composition of large scale geospatial monitoring and prediction systems from existing components. Sensor middleware standards are paving the way for the emerging sensor web which is envisioned to make millions of geospatial sensors and their data publicly accessible by providing discovery, task and query functionality over the internet. In a similar fashion, concurrent development is taking place in the field of grid computing whereby the virtualisation of computational and data storage resources using middleware abstraction provides a framework to share computing resources. Sensor web and grid computing share a common vision of world-wide connectivity and in their current form they are both realised using web services as the underlying technological framework. The integration of sensor web and grid computing middleware using open standards is expected to facilitate interoperability and scalability in near real-time geoprocessing systems. The aim of this thesis is to develop an appropriate conceptual and practical framework in which open standards in grid computing, sensor web and geospatial web services can be combined as a technological basis for the monitoring and prediction of geospatial phenomena in the earth systems domain, to facilitate real-time decision support. The primary topic of interest is how real-time sensor data can be processed on a grid computing architecture. This is addressed by creating a simple typology of real-time geoprocessing operations with respect to grid computing architectures. A geoprocessing system exemplar of each geoprocessing operation in the typology is implemented using contemporary tools and techniques which provides a basis from which to validate the standards frameworks and highlight issues of scalability and interoperability. It was found that it is possible to combine standardised web services from each of these aforementioned domains despite issues of interoperability resulting from differences in web service style and security between specifications. A novel integration method for the continuous processing of a sensor observation stream is suggested in which a perpetual processing job is submitted as a single continuous compute job. Although this method was found to be successful two key challenges remain; a mechanism for consistently scheduling real-time jobs within an acceptable time-frame must be devised and the tradeoff between efficient grid resource utilisation and processing latency must be balanced. The lack of actual implementations of distributed geoprocessing systems built using sensor web and grid computing has hindered the development of standards, tools and frameworks in this area. This work provides a contribution to the small number of existing implementations in this field by identifying potential workflow bottlenecks in such systems and gaps in the existing specifications. Furthermore it sets out a typology of real-time geoprocessing operations that are anticipated to facilitate the development of real-time geoprocessing software.EThOS - Electronic Theses Online ServiceEngineering and Physical Sciences Research Council (EPSRC) : School of Civil Engineering & Geosciences, Newcastle UniversityGBUnited Kingdo

Fault-tolerance and load management in a distributed stream processing system

Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, February 2006.This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections.Includes bibliographical references (p. 187-199).Advances in monitoring technology (e.g., sensors) and an increased demand for online information processing have given rise to a new class of applications that require continuous, low-latency processing of large-volume data streams. These "stream processing applications" arise in many areas such as sensor-based environment monitoring, financial services, network monitoring, and military applications. Because traditional database management systems are ill-suited for high-volume, low-latency stream processing, new systems, called stream processing engines (SPEs), have been developed. Furthermore, because stream processing applications are inherently distributed, and because distribution can improve performance and scalability, researchers have also proposed and developed distributed SPEs. In this dissertation, we address two challenges faced by a distributed SPE: (1) faulttolerant operation in the face of node failures, network failures, and network partitions, and (2) federated load management. For fault-tolerance, we present a replication-based scheme, called Delay, Process, and Correct (DPC), that masks most node and network failures.(cont.) When network partitions occur, DPC addresses the traditional availability-consistency trade-off by maintaining, when possible, a desired availability specified by the application or user, but eventually also delivering the correct results. While maintaining the desired availability bounds, DPC also strives to minimize the number of inaccurate results that must later be corrected. In contrast to previous proposals for fault tolerance in SPEs, DPC simultaneously supports a variety of applications that differ in their preferred trade-off between availability and consistency. For load management, we present a Bounded-Price Mechanism (BPM) that enables autonomous participants to collaboratively handle their load without individually owning the resources necessary for peak operation. BPM is based on contracts that participants negotiate offline. At runtime, participants move load only to partners with whom they have a contract and pay each other the contracted price. We show that BPM provides incentives that foster participation and leads to good system-wide load distribution. In contrast to earlier proposals based on computational economies, BPM is lightweight, enables participants to develop and exploit preferential relationships, and provides stability and predictability.(cont.) Although motivated by stream processing, BPM is general and can be applied to any federated system. We have implemented both schemes in the Borealis distributed stream processing engine. They will be available with the next release of the system.by Magdalena Balazinska.Ph.D