Data-intensive Service Mashup Based on Game Theory and Hybrid Fireworks Optimization Algorithm in the Cloud

End users can create kinds of mashups which combine various data-intensive services to form new services. The challenging issue of data-intensive service mashup is how to find service from a great deal of candidate services while satisfying SLAs. In this paper, Service-Level Agreement (SLA) consists of two parts, which are SLA-Q and SLA-T. SLA-Q (SLA-T) indicates the end-to-end QoS (transactional) requirements. SLA-aware service mashup problem is known as NP-hard, which takes a significant amount of time to find optimal solutions. The service correlation also exists in data-intensive service mashup problem. In this paper, the service correlation includes the functional correlation and QoS correlation. For efficiently solving the data-intensive service mashup problem with service correlation, we propose an approach GTHFOA-DSMSC (Data-intensive Service Mashup with Service Correlation based on Game Theory and Hybrid Fireworks Optimization Algorithm) which evolves a set of solutions to the Pareto optimal front. The experimental tests demonstrate the effectiveness of the algorithm.