Evaluation and Implementation of Machine Learning Methods for an Optimized Web Service Selection in a Future Service Market

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