Optimizing Service Selection for Probabilistic QoS Attributes

The service selection problem (SSP) – i.e., choosing from sets of functionally equivalent services in order to fulfill certain business process steps based on non-functional requirements – has frequently been addressed in literature considering deterministic values for the Quality of Service (QoS) attributes. However, the usage of deterministic values does not reflect the uncertainty about the actual value of an attribute during execution, thus ignoring the risk of QoS violations. In the paper at hand, a simulative step, based on stochastic QoS attributes, is performed as complement for optimally solving the SSP using linear programming methods. With this two-step approach, uncertainties in the selected set of services can be explicitly revealed and addressed through repeated selection steps, thus allowing to prevent the violation of QoS restrictions much more effectively.

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