Measuring service quality in service-oriented architectures using a hybrid particle swarm optimization algorithm and artificial neural network (PSO-ANN)

Web service combination is an important task performed in different phases of the service-oriented architecture lifecycle. Measuring service quality based on the non-functional characteristics is an exceedingly difficult task. Therefore, this paper presents a Multilayer Perceptron Artificial Neural Network (MLPANN) to provide a method for measuring quality of service in a service-oriented architecture. To improve network performance, Particle Swarm Optimization (PSO) is used to optimize the weights of the network. Finally, our results are compared to those of a combination of Different Evolution (DE) algorithm and MLPANN in terms of Mean Square Error (MSE), Root Mean Square Error (RMSE) and Standard Deviation (STD). The results demonstrate the superiority of the proposed method.

[1]  Farhad Ramezani,et al.  Measuring of Software Maintainability Using Adaptive Fuzzy Neural Network , 2015 .

[2]  Vahid Rafe,et al.  Considering Faults in Service-Oriented Architecture: A Graph Transformation-Based Approach , 2009, 2009 International Conference on Computer Technology and Development.

[3]  Vahid Rafe,et al.  Modeling Fault Tolerant Services in Service-Oriented Architecture , 2009, 2009 Third IEEE International Symposium on Theoretical Aspects of Software Engineering.

[4]  Deepak Dahiya,et al.  Optimized Service Discovery Using QoS Based Ranking: A Fuzzy Clustering and Particle Swarm Optimization Approach , 2011, 2011 IEEE 35th Annual Computer Software and Applications Conference Workshops.

[5]  Eyhab Al-Masri,et al.  Investigating web services on the world wide web , 2008, WWW.

[6]  Barbara Pernici,et al.  Selection of Service Adaptation Strategies Based on Fuzzy Logic , 2011, 2011 IEEE World Congress on Services.

[7]  Narasimhan Sundararajan,et al.  Self regulating particle swarm optimization algorithm , 2015, Inf. Sci..

[8]  Ioana Sora,et al.  Translating user preferences into fuzzy rules for the automatic selection of services , 2009, 2009 5th International Symposium on Applied Computational Intelligence and Informatics.

[9]  Maria Luisa Villani,et al.  An approach for QoS-aware service composition based on genetic algorithms , 2005, GECCO '05.

[10]  Richong Zhang,et al.  Predicting the Quality of Web Services Based on User Stability , 2016, 2016 IEEE International Conference on Services Computing (SCC).

[11]  Jaideep Srivastava,et al.  A probabilistic approach to modeling and estimating the QoS of web-services-based workflows , 2007, Inf. Sci..

[12]  Sudhir Agarwal,et al.  User Preference Based Automated Selection of Web Service Compositions , 2005 .

[13]  Karim Djemame,et al.  Fuzzy Logic Based QoS Optimization Mechanism for Service Composition , 2013, 2013 IEEE Seventh International Symposium on Service-Oriented System Engineering.

[14]  Kamala Manasa Dhara,et al.  A Survey Paper on Service Oriented Architecture Approach and Modern Web Services , 2015 .

[15]  Bruno Trstenjak,et al.  Predicting Quality of Web Service using IKS hybrid model , 2014, 2014 X International Symposium on Telecommunications (BIHTEL).

[16]  F. Mardukhi,et al.  A FUZZY-BASED USER-CENTRIC APPROACH FOR SELECTING THE OPTIMAL COMPOSITION OF SERVICES , 2010 .

[17]  Eyhab Al-Masri,et al.  Discovering the best web service , 2007, WWW '07.

[18]  Sanjeev Jain,et al.  An Analysis of Swarm Intelligence based Load Balancing Algorithms in a Cloud Computing Environment , 2015 .

[19]  Gail Corbitt,et al.  Service Oriented Architecture: Challenges for Business and Academia , 2008, Proceedings of the 41st Annual Hawaii International Conference on System Sciences (HICSS 2008).

[20]  Riccardo Poli,et al.  Particle swarm optimization , 1995, Swarm Intelligence.

[21]  Barbara Pernici,et al.  Evaluating Web Service QoS: A neural fuzzy approach , 2011, 2011 IEEE International Conference on Service-Oriented Computing and Applications (SOCA).

[22]  Mahmood Allameh Amiri,et al.  QoS aware web service composition based on genetic algorithm , 2010, 2010 5th International Symposium on Telecommunications.

[23]  Seyyed Reza Khaze,et al.  A New Approach in Bloggers Classification with Hybrid of K-Nearest Neighbor and Artificial Neural Network Algorithms , 2015 .

[24]  Eyhab Al-Masri,et al.  QoS-based Discovery and Ranking of Web Services , 2007, 2007 16th International Conference on Computer Communications and Networks.

[25]  Stan Matwin,et al.  A review on particle swarm optimization algorithm and its variants to clustering high-dimensional data , 2013, Artificial Intelligence Review.

[26]  Tharam S. Dillon,et al.  Fuzzy Service Quality Review in Service Oriented Architectures , 2006, 2006 IEEE International Conference on Fuzzy Systems.

[27]  Stephen J. H. Yang,et al.  An optimal QoS-based Web service selection scheme , 2009, Inf. Sci..

[28]  Jürgen Schmidhuber,et al.  Deep learning in neural networks: An overview , 2014, Neural Networks.