A Constraint Satisfaction Service Composition Method Supporting One to Many Task Pattern

The current service composition problem is usually aimed at the constrained service composition problem of Web services. However, this kind of scheme is not suitable for the situation that one service corresponds to multiple tasks. In order to solve the problem of service composition meeting business constraints and users’ needs in a specific field, this paper uses Markov decision process to model the problem, proposes a method based on deep Q-learning to solve the problem, and uses random sampling method for the inference. This method calculates the candidate services that can be used for composition, and takes the maximum cumulative rewards represented by the degree of constraint satisfaction as the optimization objective, and globally optimizes the results to meet the needs of users to the greatest extent. The experimental results show that: in this problem, compared with the existing methods, this method has higher combination efficiency.