Proactive service selection based on acquaintance model and LS-SVM

Current service selection is unable to perform proactively. When a service provider overloads, the services list is ever-lengthening, which leads to backlog and failure of service composition. To compensate for this deficiency, this paper improves the proactive service selection. In this strategy, the service provider analyses a time series of services received to forecast the backlog and consign services to the others through a negotiation process. The least squares support vector learning is used to predict a random list of services, and an acquaintance model (AM) makes a consigner allocate backlog services to other providers with high degree of relationship. The backlog of services by forecasting is entrusted to the provider who can implement the same service, and negotiation between the providers with the same role would allow generation of a new service selection solution before a fault occurs. Experiments showed that the least squares support vector machine (LS-SVM) algorithm was more accurate in predicting a services list and a negotiation mechanism using AM decreased communication time effectively, which improved the success rate of service selection and reduced the execution time of service composition.

[1]  Carlo Ghezzi,et al.  Performance‐driven dynamic service selection , 2015, Concurr. Comput. Pract. Exp..

[2]  Yevgeniy V. Bodyanskiy,et al.  Hybrid adaptive wavelet-neuro-fuzzy system for chaotic time series identification , 2013, Inf. Sci..

[3]  Uttam Bhat,et al.  Emergence of clustering in an acquaintance model without homophily , 2014, ArXiv.

[4]  Michael Luck,et al.  Efficient adaptive QoS-based service selection , 2013, Service Oriented Computing and Applications.

[5]  Li Xu,et al.  An optimizing method of RBF neural network based on genetic algorithm , 2011, Neural Computing and Applications.

[6]  Cairong Wu,et al.  Evaluation and Research on Sports Psychology based on BP Neural Network Model , 2012 .

[7]  Riste Skrekovski,et al.  Modeling acquaintance networks based on balance theory , 2014, Int. J. Appl. Math. Comput. Sci..

[8]  Kaiqi Zou,et al.  A Novel Approach for Time Series Analysis Based RBF Neural Network , 2010, 2010 International Forum on Information Technology and Applications.

[9]  Hai Yan,et al.  An Approach for Web Service QoS Dynamic Prediction , 2013, J. Softw..

[10]  Shifei Ding,et al.  An optimizing BP neural network algorithm based on genetic algorithm , 2011, Artificial Intelligence Review.

[11]  Ching-Hsien Hsu,et al.  Multi-user web service selection based on multi-QoS prediction , 2014, Inf. Syst. Frontiers.

[12]  Xiao Xue,et al.  Reliable Web service composition based on QoS dynamic prediction , 2015, Soft Comput..

[13]  Witold Pedrycz,et al.  Human-centric analysis and interpretation of time series: a perspective of granular computing , 2014, Soft Computing.

[14]  Andrzej Szalas,et al.  Paraconsistent semantics of speech acts , 2015, Neurocomputing.

[15]  Shifei Ding,et al.  Research of granular support vector machine , 2011, Artificial Intelligence Review.

[16]  Michael Luck,et al.  Reactive Service Selection in Dynamic Service Environments , 2012, ESOCC.

[17]  Vwani P. Roychowdhury,et al.  Parallel randomized sampling for support vector machine (SVM) and support vector regression (SVR) , 2008, Knowledge and Information Systems.

[18]  Shie-Jue Lee,et al.  A weighted LS-SVM based learning system for time series forecasting , 2015, Inf. Sci..