QoS-Aware web service composition with multi-objective evolutionary algorithms
Service-based applications often invoke web services provided by third parties in its workflow. The Quality of Service provided by service providers is usually expressed in terms of a Service Level Agreement, that specifies the cost, performance, availability, etc. In this scenario, intelligent systems can help the engineers to scrutinize the service market, in order to select those service configurations that best fit their needs.
This search problem, also known as a QoS-aware web services composition, needs to simultaneously take into account multiple quality attributes which may be in conflict. For instance, faster response time entails a higher cost. Therefore, several quality properties must be optimized simultaneously using multi-objective or many–objective approaches, which require computationally efficient algorithms.
This session presents the QoS-aware web services composition problem and its various variants, as well as a comparative experimental study of multi-objective and many-objective algorithms. Specifically, we explore the suitability of various evolutionary algorithms to address the problem on the basis of a set of real web services with 9 quality properties. It is observed that some algorithms can achieve a better balance between the quality properties, or even promote specific properties while maintaining high quality values for the rest. Furthermore, this search process can be performed within a reasonable computational cost, allowing its adoption by intelligent systems and enabling decision support in the field of service-oriented computing.