Efficient adaptive query processing on large database systems available in the cloud environment

Tese de Doutoramento em InformáticaNowadays, many companies are migrating their applications and data to cloud service providers, mainly because of their ability to answer quickly to business requirements. Thereby, the performance is an important requirement for most customers when they wish to migrate their applications to the cloud. Therefore, in cloud environments, resources should be acquired and released automatically and quickly at runtime. Moreover, the users and service providers expect to get answers in time to ensure the service SLA (Service Level Agreement). Consequently, ensuring the QoS (Quality of Service) is a great challenge and it increases when we have large amounts of data to be manipulated in this environment. To resolve this kind of problems, several researches have been focused on shorter execution time using adaptive query processing and/or prediction of resources based on current system status. However, they present important limitations. For example, most of these works does not use monitoring during query execution and/or presents intrusive solutions, i.e. applied to the particular context. The aim of this thesis is the development of new solutions/strategies to efficient adaptive query processing on large databases available in a cloud environment. It must integrate adaptive re-optimization at query runtime and their costs are based on the SRT (Service Response Time – SLA QoS performance parameter). Finally, the proposed solution will be evaluated on large scale with large volume of data, machines and queries in a cloud computing infrastructure. Finally, this work also proposes a new model to estimate the SRT for different request types (database access requests). This model will allow the cloud service provider and its customers to establish an appropriate SLA relative to the expected performance of the services available in the cloud.Atualmente, muitas companhias têm migrado suas aplicações e dados para fornecedores de serviços em nuvem, pois um dos principais benefícios dessa tecnologia é a capacidade de responder rapidamente às necessidades do negócio. Assim, o desempenho é um dos mais importantes requisitos para a maioria dos clientes que desejam migrar suas aplicações para a nuvem. Em ambiente de nuvem, os recursos devem ser adquiridos e libertados automaticamente e rapidamente em tempo de execução. Além disso, os utilizadores e fornecedores de serviços esperam sempre garantir o contrato SLA (Acordo de Nível de Serviço). Consequentemente, garantir o QoS (Qualidade de Serviço) é um grande desafio, que se torna mais complexo quando existe uma grande quantidade de dados a serem manipulados neste ambiente. Para resolver estes tipos de problemas, diversas pesquisas têm sido realizadas focando o menor tempo de execução dos pedidos do utilizador na nuvem usando técnicas de processamento adaptativo de consultas e/ou utilizando técnicas de predição de recursos baseados no estado atual do sistema. Contudo, esses trabalhos apresentam limitações importantes. Por exemplo, a maioria desses trabalhos não utiliza monitorazação durante a execução da consulta e/ou apresenta soluções intrusivas, isto é, aplicadas a um contexto particular. Portanto, o objetivo desta tese consiste no desenvolvimento de uma nova solução/estratégia para o processamento eficiente (adaptativo) de consultas sobre grandes bases de dados disponíveis em ambiente de nuvem. Ela irá integrar técnicas de otimização adaptativas em tempo de execução da consulta e seus custos são baseados no SRT (Tempo de Resposta do Serviço – parâmetro QoS de desempenho do SLA). A solução proposta será avaliada em larga escala utilizando uma grande base de dados, máquinas e consultas em um ambiente real de computação na nuvem. Finalmente, este trabalho também propõe um novo modelo para estimar o SRT para diferentes tipos de pedidos (pedidos de acesso a banco de dados). Este modelo permitirá que um fornecedor de serviços em nuvem e seus clientes possam estabelecer um contrato SLA adequado, relativo ao desempenho esperado dos serviços disponíveis em nuvem

Quality-of-Service-Aware Service Selection in Mobile Environments

The last decade is characterized by the rise of mobile technologies (UMTS, LTE, WLAN, Bluetooth, SMS, etc.) and devices (notebooks, tablets, mobile phones, smart watches, etc.). In this rise, mobiles phones have played a crucial role because they paved the way for mobile pervasion among the public. In addition, this development has also led to a rapid growth of the mobile service/application market (Statista 2017b). As a consequence, users of mobile devices nowadays find themselves in a mobile environment, with (almost) unlimited access to information and services from anywhere through the Internet, and can connect to other people at any time (cf. Deng et al. 2016; Newman 2015). Additionally, modern mobile devices offer the opportunity to select the services or information that best fit to a user’s current context. In this regard, mobile information services support users in retrieving context and non-context information, such as about the current traffic situation, public transport options, and flight connections, as well as about real-world entities, such as sights, museums, and restaurants (cf. Deng et al. 2016; Heinrich and Lewerenz 2015; Ventola 2014). An example of the application of mobile information services is several users planning a joint city day trip. Here, the users could utilize information retrieved about real-world entities for their planning. Such a trip constitutes a process with multiple participating users and may encompass actions such as visiting a museum and having lunch. For each action, mobile information services (e.g., Yelp, TripAdvisor, Google Places) can help locate available alternatives that differ only in attributes such as price, average length of stay (i.e., duration), or recommendations published by previous visitors. In addition, context information (e.g., business hours, distance) can be used to more effectively support the users in their decisions. Moreover, because multiple users are participating in the same trip, some users want to or must conduct certain actions together. However, decision-makers (e.g., mobile users) attempting to determine the optimal solution for such processes – meaning the best alternative for each action and each participating user – are confronted with several challenges, as shown by means of the city trip example: First, each user most likely has his or her own preferences and requirements regarding attributes such as price and duration, which all must be considered. Furthermore, for each action of the day trip, a huge number of alternatives probably exist. Thus, users might face difficulties selecting the optimal alternatives because of an information overload problem (Zhang et al. 2009). Second, taking multiple users into account may require the coordination of their actions because of potential dependencies among different users’ tours, which, for example, is the case when users prefer to conduct certain actions together. This turns the almost sophisticated decision problem at hand into a problem of high complexity. The problem complexity is increased further when considering context information, because this causes dependencies among different actions of a user that must be taken into account. For instance, the distance to cover by a user to reach a certain restaurant depends on the location of the previously visited museum. In conclusion, it might be impossible for a user to determine an optimal city trip tour for all users, making decision support by an information system necessary. Because the available alternatives for each action of the process can be denoted as (information) service objects (cf. Dannewitz et al. 2008; Heinrich and Lewerenz 2015; Hinkelmann et al. 2013), the decision problem at hand is a Quality-of-Service (QoS)-aware service selection problem. This thesis proposes novel concepts and optimization approaches for QoS-aware service selection regarding processes with multiple users and context information, focusing on scenarios in mobile environments. In this respect, the developed multi user context-aware service selection approaches are able to deal with dependencies among different users’ service compositions, which result from the consideration of multiple users, as well as dependencies within a user’s service composition, which result from the consideration of context information. Consequently, these approaches provide suitable support for decision-makers, such as mobile users