Alliance-Aware Service Composition with Efficient Matching Search

Service bundling within enterprises and service cooperation outside enterprises are quite common on cloud. The correlations between services called Alliance Relation (AR) have great impact on the system QoS of service composition. Existing research have not fully and systematically considered the types of AR. Meanwhile, many works suffer from the low searching efficiency of finding the optimal matching AR for composite services. In this paper, we propose a novel approach called Q-ARIGraph-NSGA3, where we establish a multi-granularity optimization model on quotient space and an efficient matching search method in which a partition mechanism is applied to accelerate the generation of the graph. We expand the alliance relation types in service composition with service granulation and AR granulation employed concurrently. Extensive experiments are conducted on a real-world web service dataset, which demonstrate that our approach outperforms the state-of-the-art approaches in terms of effectiveness and efficiency.