A Graph Neural Network and Pointer Network Based Approach for QoS Aware Service Composition

Quality-of-service (QoS)-aware service composition aims to aggregate multiple existing services to meet users’ complex functional and nonfunctional requirements that cannot be met by simple services. The accumulation of user tasks and service composition solutions makes it possible to mine empirical rules from those historical compositions to reduce the search space and thus improve the composition efficiency. Traditional empirical rule-based methods focus on mining with well-designed rules, ignoring the underlying correlations between tasks and between services. Meanwhile, infrequently used services are not valued by these methods, but these services may still be used in constructing optimal service solutions. In addition, many methods use reinforcement learning to compose services to efficiently construct service solutions, but they do not achieve the same effect as traditional metaheuristic methods. In view of the above shortcomings, considering the ability of graphs to express relationships, we first construct tasks and services as graphs and then use a graph neural network (GNN) to mine underlying correlations and predict the probability that each service will be used to construct the solution corresponding to the task. Next, based on these high-probability services, we utilize pointer network (PN)-based reinforcement learning to efficiently construct the initial service solution. The PN is often used to solve combinatorial optimization problems and is noninferior to metaheuristics for small-scale data. To increase the generalization ability of the network, we superimpose another layer on the PN. Finally, to take advantage of infrequently used services, we use the local whale optimization algorithm (WOA) to fine-tune the initial solution and obtain a high-quality solution. The experimental results show that our approach outperforms several existing methods in terms of both composition efficiency and solution quality.