Supervised Web Service Composition Integrating Multi-objective QoS Optimization and Service Quantity Minimization

The QoS of web service has been increasingly crucial due to the escalating number of services with similar or identical functionality, which leads to intensive researches on QoS-aware web service composition. Correspondingly, to optimize not only QoS but also service quantity in a composition has also been increasingly challenging. Currently, there are already many researches on service composition addressing the optimization of multiple QoS attributes, but it is still rare to take service quantity as an optimization objective as well. To address this issue, this paper proposes a novel supervised web service composition mechanism integrating multi-objective QoS optimization and the minimization of service quantity. Firstly a memory-based search algorithm is proposed to compute each single-objective optimal QoS, after which a knapsack-variant algorithm is applied to minimize the number of services without considering the QoS. Finally, a supervised multi-objective optimization is performed based on the above single-objective optimization results. Experimental results on both Web Service Challenge 2009’s datasets and substantial datasets randomly generated show that the proposed service composition method outperforms the state-of-the-arts by achieving a much better tradeoff among all the objectives.

[1]  Manuel Mucientes,et al.  Hybrid Optimization Algorithm for Large-Scale QoS-Aware Service Composition , 2015, IEEE Transactions on Services Computing.

[2]  Aurora Trinidad Ramirez Pozo,et al.  Many-Objective Evolutionary Algorithms in the Composition of Web Services , 2010, 2010 Eleventh Brazilian Symposium on Neural Networks.

[3]  Ansuman Banerjee,et al.  A Scalable and Approximate Mechanism for Web Service Composition , 2015, 2015 IEEE International Conference on Web Services.

[4]  Bofeng Zhang,et al.  Towards Uncertain QoS-Aware Service Composition via Multi-Objective Optimization , 2017, 2017 IEEE International Conference on Web Services (ICWS).

[5]  Manuel Mucientes,et al.  A Dynamic QoS-Aware Semantic Web Service Composition Algorithm , 2012, ICSOC.

[6]  Min Chen,et al.  Redundant Service Removal in QoS-Aware Service Composition , 2012, 2012 IEEE 19th International Conference on Web Services.

[7]  Athman Bouguettaya,et al.  Efficient Service Skyline Computation for Composite Service Selection , 2013, IEEE Transactions on Knowledge and Data Engineering.

[8]  Shi-Liang Fan,et al.  Efficient Web Service Composition via Knapsack-Variant Algorithm , 2018, SCC.

[9]  Minjie Zhang,et al.  Multi-Objective Service Composition Using Reinforcement Learning , 2013, ICSOC.

[10]  Manuel Mucientes,et al.  A Hybrid Local-Global Optimization Strategy for QoS-Aware Service Composition , 2015, 2015 IEEE International Conference on Web Services.

[11]  Fuyuki Ishikawa,et al.  QoS-Aware Automatic Service Composition by Applying Functional Clustering , 2011, 2011 IEEE International Conference on Web Services.

[12]  Yixin Chen,et al.  QoS-Aware Dynamic Composition of Web Services Using Numerical Temporal Planning , 2014, IEEE Transactions on Services Computing.

[13]  Wei Jiang,et al.  QSynth: A Tool for QoS-aware Automatic Service Composition , 2010, 2010 IEEE International Conference on Web Services.

[14]  Kai Hwang,et al.  Skyline Discovery and Composition of Multi-Cloud Mashup Services , 2016, IEEE Transactions on Services Computing.

[15]  Yu-Bin Yang,et al.  Web Service Composition Integrating QoS Optimization and Redundancy Removal , 2013, 2013 IEEE 20th International Conference on Web Services.

[16]  Anja Strunk QoS-Aware Service Composition: A Survey , 2010, 2010 Eighth IEEE European Conference on Web Services.

[17]  Anne H. H. Ngu,et al.  QoS-aware middleware for Web services composition , 2004, IEEE Transactions on Software Engineering.