Title: A feature-driven variability-enabled approach to adaptive service compositions

Authors: Chang-ai Sun; Zhen Wang; Zaixing Zhang; Luo Xu; Jun Han; Yanbo Han

Addresses: School of Computer and Communication Engineering, University of Science and Technology Beijing, Beijing, 100083, China ' School of Computer and Communication Engineering, University of Science and Technology Beijing, Beijing, 100083, China ' School of Computer and Communication Engineering, University of Science and Technology Beijing, Beijing, 100083, China ' North China Institute of Computing Technology, Beijing, 100083, China ' Department of Computer Science and Software Engineering, Swinburne University of Technology, Hawthorn, VIC 3122, Australia ' Beijing Key Laboratory on Integration and Analysis of Large-Scale Stream Data, North China University of Technology, Beijing, 100144, China

Abstract: Service compositions are widely used to construct complex applications. Due to the frequent changes of environment and requirements, service compositions need to be adaptable enough. In this work, we propose a feature-driven variability-enabled adaptive service composition approach to systematically treat the variability in the full life-cycle of service compositions. Specifically, the feature model is introduced to represent common and variable requirements and drive the variability design of service compositions. An abstract service composition model is used to define the variable business process. Rules and algorithms are then defined to transform the feature model to the abstract service composition model, from which different process instances are derived on demand to meet different requirements. We have developed a prototype tool to facilitate and automate our approach as much as possible. Finally, a case study is conducted to demonstrate the proposed approach and validate its effectiveness and efficiency.

Keywords: variability management; adaptive service compositions; abstract service composition model; feature model; model transformation.

DOI: 10.1504/IJWGS.2023.129337

International Journal of Web and Grid Services, 2023 Vol.19 No.1, pp.79 - 112

Received: 06 Apr 2022
Received in revised form: 22 Oct 2022
Accepted: 14 Nov 2022
Published online: 06 Mar 2023 *