A Novel Web Service Quality Prediction Framework Based on F-ELM

With the prevalence of service computing and cloud computing, more and more services are emerging and running on highly dynamic and changing environments (The Web). Under these uncontrollable circumstances, these services will generate huge volumes of data, such as trace log, QoS (Quality of Service) information and WSDL files. It is impractical to monitor the changes in QoS parameters for each and every service in order to timely trigger precaution, due to high computational costs associated with the process. In order to overcome the above problem, this paper proposes a web service quality prediction method based on improved Extreme Learning Machine with feature optimization. First, we extract web service trace logs and QoS information from the service log and convert them into feature vectors. Furthermore, in order to actively respond ELM more quickly, we mine early feature subsets in advance by developing a feature mining algorithm, named FS, and apply such feature subsets to ELM for training (F-ELM). Experimental results prove that F-ELM (trained by the selected feature subsets) can efficiently lift the reliability of service quality and improve the earliness of prediction.

[1]  Zibin Zheng,et al.  QoS-Aware Web Service Recommendation by Collaborative Filtering , 2011, IEEE Transactions on Services Computing.

[2]  Ji Su Park,et al.  Markov Chain Based Monitoring Service for Fault Tolerance in Mobile Cloud Computing , 2011, 2011 IEEE Workshops of International Conference on Advanced Information Networking and Applications.

[3]  Binu P. Chacko,et al.  Handwritten character recognition using wavelet energy and extreme learning machine , 2012, Int. J. Mach. Learn. Cybern..

[4]  Ge Yu,et al.  Maximal Subspace Coregulated Gene Clustering , 2008, IEEE Transactions on Knowledge and Data Engineering.

[5]  Jeffrey Xu Yu,et al.  Learning Phenotype Structure Using Sequence Model , 2014, IEEE Transactions on Knowledge and Data Engineering.

[6]  Zibin Zheng,et al.  Personalized Web Service Recommendation via Normal Recovery Collaborative Filtering , 2013, IEEE Transactions on Services Computing.

[7]  Liang Chen,et al.  Composite Service Recommendation Based on Bayes Theorem , 2012, Int. J. Web Serv. Res..

[8]  Korris Fu-Lai Chung,et al.  Positive and negative fuzzy rule system, extreme learning machine and image classification , 2011, Int. J. Mach. Learn. Cybern..

[9]  Xizhao Wang,et al.  Upper integral network with extreme learning mechanism , 2011, Neurocomputing.

[10]  Guoren Wang,et al.  Finding Novel Diagnostic Gene Patterns Based on Interesting Non-redundant Contrast Sequence Rules , 2011, 2011 IEEE 11th International Conference on Data Mining.

[11]  Jacek M. Zurada,et al.  Advances in Neural Networks - ISNN 2006, Third International Symposium on Neural Networks, Chengdu, China, May 28 - June 1, 2006, Proceedings, Part I , 2006, International Symposium on Neural Networks.

[12]  Xunkai Wei,et al.  Comparative Study of Extreme Learning Machine and Support Vector Machine , 2006, ISNN.

[13]  Narasimhan Sundararajan,et al.  Fully complex extreme learning machine , 2005, Neurocomputing.

[14]  Dianhui Wang,et al.  Extreme learning machines: a survey , 2011, Int. J. Mach. Learn. Cybern..

[15]  Jian Pei,et al.  2012- Data Mining. Concepts and Techniques, 3rd Edition.pdf , 2012 .

[16]  Zhaohui Wu,et al.  Collaborative Web Service QoS Prediction with Location-Based Regularization , 2012, 2012 IEEE 19th International Conference on Web Services.

[17]  Chee Kheong Siew,et al.  Extreme learning machine: Theory and applications , 2006, Neurocomputing.

[18]  Hongming Zhou,et al.  Optimization method based extreme learning machine for classification , 2010, Neurocomputing.

[19]  Manas Ranjan Patra,et al.  Web-services classification using intelligent techniques , 2010, Expert Syst. Appl..

[20]  Robert K. L. Gay,et al.  Error Minimized Extreme Learning Machine With Growth of Hidden Nodes and Incremental Learning , 2009, IEEE Transactions on Neural Networks.

[21]  Alfredo Goldman,et al.  On Graph Reduction for QoS Prediction of Very Large Web Service Compositions , 2012, 2012 IEEE Ninth International Conference on Services Computing.

[22]  Qian Tao,et al.  A novel prediction approach for trustworthy QoS of web services , 2012, Expert Syst. Appl..

[23]  Ling Liu,et al.  Stock Market Volatility Prediction: A Service-Oriented Multi-kernel Learning Approach , 2012, 2012 IEEE Ninth International Conference on Services Computing.

[24]  Corinna Cortes,et al.  Support-Vector Networks , 1995, Machine Learning.

[25]  Chee Kheong Siew,et al.  Extreme learning machine: RBF network case , 2004, ICARCV 2004 8th Control, Automation, Robotics and Vision Conference, 2004..