A probabilistic approach for long-term B2B service compositions

Service composition algorithms are used for realizing loosely coupled interactions in Service-Oriented Computing. Starting from an abstract workflow, concrete services are matched, based on their QoS, with the preferences and constraints of users. Current approaches usually only consider static QoS values and find a single solution consisting of one concrete service for each workflow task. In a business-to-business (B2B) environment, though, there are additional requirements for service compositions: 1) a high number of invocations, and 2) a high reliability. Thus, we introduce a probabilistic approach on the basis of a new QoS model to solve the composition problem for such long-term B2B service compositions. For each task and for every point in time, we determine the most appropriate services and backup services for a specific user. Thus, the selection depends on the actual response time and reliability, or recent invocation failures or timeouts. For that purpose, we propose an adaptive genetic algorithm that employs our QoS model and determines backup services dynamically based on the required reliability. Our evaluations show that our approach significantly increases the utility of long-term compositions compared with standard approaches in the envisioned B2B environments.