In future service markets a selection of functionally equal services is omnipresent. The evolving challenge, finding the best-fit service, requires a distinction between the non-functional service characteristics (e.g., response time, price, availability). Service providers commonly capture those quality characteristics in so-called Service Level Agreements (SLAs). However, a service selection based on SLAs is inadequate, because the static SLAs generally do not consider the dynamic service behaviors and quality changes in a service-oriented environment. Furthermore, the profit-oriented service providers tend to embellish their SLAs by flexibly handling their correctness. Within the SOC (Service Oriented Computing) research project of the Karlsruhe University of Applied Sciences and the Linnaeus University of Sweden, a service broker framework for an optimized web service selection is introduced. Instead of relying on the providers’ quality assertions, a distributed knowledge is developed by automatically monitoring and measuring the service quality during each service consumption. The broker aims at optimizing the service selection based on the past real service performances and the defined quality preferences of a unique consumer.This thesis work concerns the design, implementation and evaluation of appropriate machine learning methods with focus on the broker’s best-fit web service selection. Within the time-critical service optimization the performance and scalability of the broker’s machine learning plays an important role. Therefore, high- performance algorithms for predicting the future non-functional service characteristics within a continuous machine learning process were implemented. The introduced so-called foreground-/background-model enables to separate the real-time request for a best-fit service selection from the time-consuming machine learning. The best-fit services for certain consumer call contexts (e.g., call location and time, quality preferences) are continuously pre-determined within the asynchronous background-model. Through this any performance issues within the critical path from the service request up to the best-fit service recommendation are eliminated. For evaluating the implemented best-fit service selection a sophisticated test data scenario with real-world characteristics was created showing services with different volatile performances, cyclic performance behaviors and performance changes in the course of time. Besides the significantly improved performance, the new implementation achieved an overall high selection accuracy. It was possible to determine in 70% of all service optimizations the actual best-fit service and in 94% of all service optimizations the actual two best-fit services.

Seleção de serviços em composições distribuídas considera principalmente a qualidade de serviço que atenda requisitos estabelecidos pelo usuário, como por exemplo, preço. No entanto, problemas relacionados a execução de composições de serviços podem ocorrer quando não se considera aspectos relacionados à rede e ao hardware, que afetam diretamente o desempenho da composição. Esse problema se agrava em composições coreografadas, pois a característica descentralizada requer um maior esforço para que essas informações possam ser consideradas em uma perspectiva global. Dessa forma, apesar da descentralização apresentar vantagens, é necessário que requisitos de qualidade de serviço da composição também sejam considerados em coreografias de serviços web para que a escolha de serviços para desempenhar um papel leve em consideração parâmetros importantes que podem afetar no desempenho da composição. Este trabalho apresenta um mecanismo, implementado sobre o framework OpenKnowledge, para selecionar serviços web em ambientes coreografados considerando primeiramente estimativas de atraso, taxa de perda e por fim considera a utilização de outros parâmetros, como utilização de CPU. Os primeiros experimentos em diferentes cenários de rede confirmaram as vantagens da proposta em relação a um seletor de serviços que ignora aspectos relacionados com a rede. Obteve-se ganhos de 20 a 97% no que diz respeito ao tempo total da execução da coreografia. Em seguida, experimentos inserindo utilização de CPU na escolha dos serviços confirmaram as vantagens de utilização de diferentes parâmetros para seleção de serviços em coreografias. / Selection of services in distributed compositions mainly considers the quality of service that meets requirements set by the user, such as price. However, problems related to the execution of service compositions may occur when aspects related to network and hardware, which directly affect the performance of the composition, are not taken in consideration. This problem is aggravated in choreographed compositions because the decentralized feature requires a greater effort so that this information may be considered in a global perspective. Thus, despite the decentralization advantages, it is necessary that the service quality requirements of the composition may also be considered in the choreography of web services so that the choice of services may play a role that takes into account important parameters, which can affect the performance of the composition. This dissertation presents a mechanism, implemented on the OpenKnowledge framework, to select web services in choreographed environments. First, considering delay estimates, loss rate and finally the use of other parameters, such as CPU utilization. The first experiments on different network scenarios confirmed the proposal advantages in relation to a selection mechanism that bypasses aspects of the network. It was obtained gains from 20% to 97% considering the total time of execution of the choreography. Then, experiments inserting CPU utilization in the choice of services confirmed the advantages of using different parameters for selection of services in choreography.