A large scale transactional service selection approach based on skyline and ant colony optimization algorithm

Quality of Service plays an increasingly important role during the procedure of web service selection. However, with the rapid growth in the number of web services, it becomes difficult to solve the service selection problem quickly. In order to improve the time cost and optimality of service selection, we propose a large scale transactional service selection approach based on Skyline and Ant Colony Optimization algorithm (ACO) to realize the near-to-optimal QoS service selection. The main idea is to take the advantage of Skyline to reduce candidate services for transactional service selection. We first use Skyline to trim the redundant service, then utilize the Ant Colony Optimization algorithm to select the service from the candidate services. Finally, this approach is evaluated experimentally based on a standard, real dataset as well as synthetically generated datasets. It reveals encouraging results in terms of the quality of solutions.