Service Pattern Optimization: Focusing on Collaboration in Service Ecosystems

The service pattern is an abstraction of the business relationship among various participants from the service ecosystem in four aspects: workflow, data flow, resource flow, and value flow. In order to optimize service patterns, it is necessary to consider the collaboration between participants as well as the interaction among different servers. The existing works either optimize the former by adjusting service orchestration, such as business process optimization and workflow optimization, or focus on the latter through adjusting service distribution, such as cloud service distribution optimization and edge service deployment optimization. However, the prevalence of service ecosystems and distributed computing has begun to make multi-user, multi-server scenarios commonplace, placing greater importance on fast and effective optimization of service patterns. In this work, we summarize the constraints and objectives and formally define the service pattern optimization problem. Beyond that, we propose a service pattern optimization-oriented confidence aware recurrent simulated annealing algorithm (PooCa). Experiments conducted on an existing dataset show that our method outperforms the other three baselines on the overall dataset as well as on the eight subsets. Also, our method can reduce the number of search iterations by 41.15% on average with the same search space. We also carry out case studies on the online travel booking service pattern and investigate factors that make patterns perform better.