DeepQSC: a GNN and Attention Mechanism-based Framework for QoS-aware Service Composition

Abstract

When several Web services with simple functions need to be combined to provide more complex functions, how to choose from a large number of Web services with the same functions but different quality of service is a QoS-based service composition problem. Currently, there are many classical methods and reinforcement learning methods applied to the QoS-based service composition problem. However, these methods require long computation time. We address three challenges in building an end-to-end supervised learning framework. 1) The number of Web services composing different composite services varies. 2) The topological relationships among Web services are difficult to express and difficult to integrate into neural networks. 3) The number of Web services providing each sub-function in composite services varies. Finally, we propose DeepQSC, a deep supervised learning framework based on graph convolutional networks and attention mechanisms. The framework can form high QoS composite services with limited computation time. We conducted experiments on a real-world dataset. The experiments show that DeepQSC has a significant advantage over six current state-of-the-art algorithms.