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Towards Optimal and Scalable Non-functional Service Matchmaking Techniques

Service-orientation paves the way for the Internet of Services (IoS), where millions of services will be available for building novel applications. As such, the service non-functional aspect should be considered for filtering and selecting among the great number of functionally-equivalent services that will be available for a specific user task. Until now, the state-of-the-art work in non-functional service discovery has exploited constraint solving techniques to optimize the matchmaking time between a non-functional service offer and demand pair. However, as matchmaking time is proportional to the offer number, this work does not scale well so it is not yet appropriate for the IoS. To this end, two alternative techniques are proposed to improve the overall matchmaking time. Both techniques were theoretically and experimentally evaluated. The results show that both techniques optimize the matchmaking time without sacrificing accuracy, while the second technique is quite scalable.