engleski

A probabilistic quality of service model for nondeterministic service compositions.

Cloud computing becomes the prevailing aspect of software engineering. In Service-Oriented Architecture (SOA), various atomic services respond to various client requests in a cloud. Selecting the actual service instance to process a particular request can be an issue when requirements for multiple Quality of Service (QoS) properties need to be satisfied for many users simultaneously. The problem becomes more complex if we take into account the compositeness of users’ applications, which consist of many tasks with non-deterministic execution plan, where QoS properties are calculated over the whole composition. In this thesis, a fast heuristic model for multi-criteria service selection is proposed, designed for multi-user composite workflows with the goal of satisfying as many as possible of the given QoS requirements. To develop such a method, some essential tools were developed, such as probabilistic formulas for estimating QoS in non-deterministic service compositions. Another contribution is a real-time adaptive QoS prediction model, which can be used before service selection to provide its input – the QoS values for atomic services. The extensive experiments have demostrated the accuracy and efficiency of the proposed models compared to the existing ones.

web services ; quality of service ; service composition ; QoS prediction ; service selection ; collaborative filtering ; transportation problem

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