137 research outputs found

    A Service-Oriented Approach for Network-Centric Data Integration and Its Application to Maritime Surveillance

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    Maritime-surveillance operators still demand for an integrated maritime picture better supporting international coordination for their operations, as looked for in the European area. In this area, many data-integration efforts have been interpreted in the past as the problem of designing, building and maintaining huge centralized repositories. Current research activities are instead leveraging service-oriented principles to achieve more flexible and network-centric solutions to systems and data integration. In this direction, this article reports on the design of a SOA platform, the Service and Application Integration (SAI) system, targeting novel approaches for legacy data and systems integration in the maritime surveillance domain. We have developed a proof-of-concept of the main system capabilities to assess feasibility of our approach and to evaluate how the SAI middleware architecture can fit application requirements for dynamic data search, aggregation and delivery in the distributed maritime domain

    Application Aware for Byzantine Fault Tolerance

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    Driven by the need for higher reliability of many distributed systems, various replication-based fault tolerance technologies have been widely studied. A prominent technology is Byzantine fault tolerance (BFT). BFT can help achieve high availability and trustworthiness by ensuring replica consistency despite the presence of hardware failures and malicious faults on a small portion of the replicas. However, most state-of-the-art BFT algorithms are designed for generic stateful applications that require the total ordering of all incoming requests and the sequential execution of such requests. In this dissertation research, we recognize that a straightforward application of existing BFT algorithms is often inappropriate for many practical systems: (1) not all incoming requests must be executed sequentially according to some total order and doing so would incur unnecessary (and often prohibitively high) runtime overhead and (2) a sequential execution of all incoming requests might violate the application semantics and might result in deadlocks for some applications. In the past four and half years of my dissertation research, I have focused on designing lightweight BFT solutions for a number of Web services applications (including a shopping cart application, an event stream processing application, Web service business activities (WS-BA), and Web service atomic transactions (WS-AT)) by exploiting application semantics. The main research challenge is to identify how to minimize the use of Byzantine agreement steps and enable concurrent execution of requests that are commutable or unrelated. We have shown that the runtime overhead can be significantly reduced by adopting our lightweight solutions. One limitation for our solutions is that it requires intimate knowledge on the application design and implementation, which may be expensive and error-prone to design such BFT solutions on complex applications. Recognizing this limitation, we investigated the use of Conflict-free Replicated Data Types (CRDTs) to

    Application Aware for Byzantine Fault Tolerance

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    Driven by the need for higher reliability of many distributed systems, various replication-based fault tolerance technologies have been widely studied. A prominent technology is Byzantine fault tolerance (BFT). BFT can help achieve high availability and trustworthiness by ensuring replica consistency despite the presence of hardware failures and malicious faults on a small portion of the replicas. However, most state-of-the-art BFT algorithms are designed for generic stateful applications that require the total ordering of all incoming requests and the sequential execution of such requests. In this dissertation research, we recognize that a straightforward application of existing BFT algorithms is often inappropriate for many practical systems: (1) not all incoming requests must be executed sequentially according to some total order and doing so would incur unnecessary (and often prohibitively high) runtime overhead and (2) a sequential execution of all incoming requests might violate the application semantics and might result in deadlocks for some applications. In the past four and half years of my dissertation research, I have focused on designing lightweight BFT solutions for a number of Web services applications (including a shopping cart application, an event stream processing application, Web service business activities (WS-BA), and Web service atomic transactions (WS-AT)) by exploiting application semantics. The main research challenge is to identify how to minimize the use of Byzantine agreement steps and enable concurrent execution of requests that are commutable or unrelated. We have shown that the runtime overhead can be significantly reduced by adopting our lightweight solutions. One limitation for our solutions is that it requires intimate knowledge on the application design and implementation, which may be expensive and error-prone to design such BFT solutions on complex applications. Recognizing this limitation, we investigated the use of Conflict-free Replicated Data Types (CRDTs) to

    Data semantic enrichment for complex event processing over IoT Data Streams

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    This thesis generalizes techniques for processing IoT data streams, semantically enrich data with contextual information, as well as complex event processing in IoT applications. A case study for ECG anomaly detection and signal classification was conducted to validate the knowledge foundation

    Processamento de eventos complexos como serviço em ambientes multi-nuvem

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    Orientadores: Luiz Fernando Bittencourt, Miriam Akemi Manabe CapretzTese (doutorado) - Universidade Estadual de Campinas, Instituto de ComputaçãoResumo: O surgimento das tecnologias de dispositivos mĂłveis e da Internet das Coisas, combinada com avanços das tecnologias Web, criou um novo mundo de Big Data em que o volume e a velocidade da geração de dados atingiu uma escala sem precedentes. Por ser uma tecnologia criada para processar fluxos contĂ­nuos de dados, o Processamento de Eventos Complexos (CEP, do inglĂȘs Complex Event Processing) tem sido frequentemente associado a Big Data e aplicado como uma ferramenta para obter informaçÔes em tempo real. Todavia, apesar desta onda de interesse, o mercado de CEP ainda Ă© dominado por soluçÔes proprietĂĄrias que requerem grandes investimentos para sua aquisição e nĂŁo proveem a flexibilidade que os usuĂĄrios necessitam. Como alternativa, algumas empresas adotam soluçÔes de baixo nĂ­vel que demandam intenso treinamento tĂ©cnico e possuem alto custo operacional. A fim de solucionar esses problemas, esta pesquisa propĂ”e a criação de um sistema de CEP que pode ser oferecido como serviço e usado atravĂ©s da Internet. Um sistema de CEP como Serviço (CEPaaS, do inglĂȘs CEP as a Service) oferece aos usuĂĄrios as funcionalidades de CEP aliadas Ă s vantagens do modelo de serviços, tais como redução do investimento inicial e baixo custo de manutenção. No entanto, a criação de tal serviço envolve inĂșmeros desafios que nĂŁo sĂŁo abordados no atual estado da arte de CEP. Em especial, esta pesquisa propĂ”e soluçÔes para trĂȘs problemas em aberto que existem neste contexto. Em primeiro lugar, para o problema de entender e reusar a enorme variedade de procedimentos para gerĂȘncia de sistemas CEP, esta pesquisa propĂ”e o formalismo Reescrita de Grafos com Atributos para GerĂȘncia de Processamento de Eventos Complexos (AGeCEP, do inglĂȘs Attributed Graph Rewriting for Complex Event Processing Management). Este formalismo inclui modelos para consultas CEP e transformaçÔes de consultas que sĂŁo independentes de tecnologia e linguagem. Em segundo lugar, para o problema de avaliar estratĂ©gias de gerĂȘncia e processamento de consultas CEP, esta pesquisa apresenta CEPSim, um simulador de sistemas CEP baseado em nuvem. Por fim, esta pesquisa tambĂ©m descreve um sistema CEPaaS fundamentado em ambientes multi-nuvem, sistemas de gerĂȘncia de contĂȘineres e um design multiusuĂĄrio baseado em AGeCEP. Para demonstrar sua viabilidade, o formalismo AGeCEP foi usado para projetar um gerente autĂŽnomo e um conjunto de polĂ­ticas de auto-gerenciamento para sistemas CEP. AlĂ©m disso, o simulador CEPSim foi minuciosamente avaliado atravĂ©s de experimentos que demonstram sua capacidade de simular sistemas CEP com acurĂĄcia e baixo custo adicional de processamento. Por fim, experimentos adicionais validaram o sistema CEPaaS e demonstraram que o objetivo de oferecer funcionalidades CEP como um serviço escalĂĄvel e tolerante a falhas foi atingido. Em conjunto, esses resultados confirmam que esta pesquisa avança significantemente o estado da arte e tambĂ©m oferece novas ferramentas e metodologias que podem ser aplicadas Ă  pesquisa em CEPAbstract: The rise of mobile technologies and the Internet of Things, combined with advances in Web technologies, have created a new Big Data world in which the volume and velocity of data generation have achieved an unprecedented scale. As a technology created to process continuous streams of data, Complex Event Processing (CEP) has been often related to Big Data and used as a tool to obtain real-time insights. However, despite this recent surge of interest, the CEP market is still dominated by solutions that are costly and inflexible or too low-level and hard to operate. To address these problems, this research proposes the creation of a CEP system that can be offered as a service and used over the Internet. Such a CEP as a Service (CEPaaS) system would give its users CEP functionalities associated with the advantages of the services model, such as no up-front investment and low maintenance cost. Nevertheless, creating such a service involves challenges that are not addressed by current CEP systems. This research proposes solutions for three open problems that exist in this context. First, to address the problem of understanding and reusing existing CEP management procedures, this research introduces the Attributed Graph Rewriting for Complex Event Processing Management (AGeCEP) formalism as a technology- and language-agnostic representation of queries and their reconfigurations. Second, to address the problem of evaluating CEP query management and processing strategies, this research introduces CEPSim, a simulator of cloud-based CEP systems. Finally, this research also introduces a CEPaaS system based on a multi-cloud architecture, container management systems, and an AGeCEP-based multi-tenant design. To demonstrate its feasibility, AGeCEP was used to design an autonomic manager and a selected set of self-management policies. Moreover, CEPSim was thoroughly evaluated by experiments that showed it can simulate existing systems with accuracy and low execution overhead. Finally, additional experiments validated the CEPaaS system and demonstrated it achieves the goal of offering CEP functionalities as a scalable and fault-tolerant service. In tandem, these results confirm this research significantly advances the CEP state of the art and provides novel tools and methodologies that can be applied to CEP researchDoutoradoCiĂȘncia da ComputaçãoDoutor em CiĂȘncia da Computação140920/2012-9CNP

    Complex Event Processing as a Service in Multi-Cloud Environments

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    The rise of mobile technologies and the Internet of Things, combined with advances in Web technologies, have created a new Big Data world in which the volume and velocity of data generation have achieved an unprecedented scale. As a technology created to process continuous streams of data, Complex Event Processing (CEP) has been often related to Big Data and used as a tool to obtain real-time insights. However, despite this recent surge of interest, the CEP market is still dominated by solutions that are costly and inflexible or too low-level and hard to operate. To address these problems, this research proposes the creation of a CEP system that can be offered as a service and used over the Internet. Such a CEP as a Service (CEPaaS) system would give its users CEP functionalities associated with the advantages of the services model, such as no up-front investment and low maintenance cost. Nevertheless, creating such a service involves challenges that are not addressed by current CEP systems. This research proposes solutions for three open problems that exist in this context. First, to address the problem of understanding and reusing existing CEP management procedures, this research introduces the Attributed Graph Rewriting for Complex Event Processing Management (AGeCEP) formalism as a technology- and language-agnostic representation of queries and their reconfigurations. Second, to address the problem of evaluating CEP query management and processing strategies, this research introduces CEPSim, a simulator of cloud-based CEP systems. Finally, this research also introduces a CEPaaS system based on a multi-cloud architecture, container management systems, and an AGeCEP-based multi-tenant design. To demonstrate its feasibility, AGeCEP was used to design an autonomic manager and a selected set of self-management policies. Moreover, CEPSim was thoroughly evaluated by experiments that showed it can simulate existing systems with accuracy and low execution overhead. Finally, additional experiments validated the CEPaaS system and demonstrated it achieves the goal of offering CEP functionalities as a scalable and fault-tolerant service. In tandem, these results confirm this research significantly advances the CEP state of the art and provides novel tools and methodologies that can be applied to CEP research

    Automatic Generation of Distributed Runtime Infrastructure for Internet of Things

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    Ph. D. ThesisThe Internet of Things (IoT) represents a network of connected devices that are able to cooperate and interact with each other in order to reach a particular goal. To attain this, the devices are equipped with identifying, sensing, networking and processing capabilities. Cloud computing, on the other hand, is the delivering of on-demand computing services – from applications, to storage, to processing power – typically over the internet. Clouds bring a number of advantages to distributed computing because of highly available pool of virtualized computing resource. Due to the large number of connected devices, real-world IoT use cases may generate overwhelmingly large amounts of data. This prompts the use of cloud resources for processing, storage and analysis of the data. Therefore, a typical IoT system comprises of a front-end (devices that collect and transmit data), and back-end – typically distributed Data Stream Management Systems (DSMSs) deployed on the cloud infrastructure, for data processing and analysis. Increasingly, new IoT devices are being manufactured to provide limited execution environment on top of their data sensing and transmitting capabilities. This consequently demands a change in the way data is being processed in a typical IoT-cloud setup. The traditional, centralised cloud-based data processing model – where IoT devices are used only for data collection – does not provide an efficient utilisation of all available resources. In addition, the fundamental requirements of real-time data processing such as short response time may not always be met. This prompts a new processing model which is based on decentralising the data processing tasks. The new decentralised architectural pattern allows some parts of data streaming computation to be executed directly on edge devices – closer to where the data is collected. Extending the processing capabilities to the IoT devices increases the robustness of applications as well as reduces the communication overhead between different components of an IoT system. However, this new pattern poses new challenges in the development, deployment and management of IoT applications. Firstly, there exists a large resource gap between the two parts of a typical IoT system (i.e. clouds and IoT devices); hence, prompting a new approach for IoT applications deployment and management. Secondly, the new decentralised approach necessitates the deployment of DSMS on distributed clusters of heterogeneous nodes resulting in unpredictable runtime performance and complex fault characteristics. Lastly, the environment where DSMSs are deployed is very dynamic due to user or device mobility, workload variation, and resource availability. In this thesis we present solutions to address the aforementioned challenges. We investigate how a high-level description of a data streaming computation can be used to automatically generate a distributed runtime infrastructure for Internet of Things. Subsequently, we develop a deployment and management system capable of distributing different operators of a data streaming computation onto different IoT gateway devices and cloud infrastructure. To address the other challenges, we propose a non-intrusive approach for performance evaluation of DSMSs and present a protocol and a set of algorithms for dynamic migration of stateful data stream operators. To improve our migration approach, we provide an optimisation technique which provides minimal application downtime and improves the accuracy of a data stream computation
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