Input Projection Algorithms Influence in Prediction and Optimization of QoS Accuracy
Keywords:
Quality of Service (QoS), adaptive models, web services, large/big dataAbstract
Regardless of new achievements in the research of prediction models, QoS is still a great issue for high quality web services and remains one of the key subjects that need to be studied. We believe that QoS should not only be measured, but have to be predicted in development and implementation phases. In this paper we assess how different input projection algorithms influence the prediction accuracy of a Multi-Layer Perceptron (MLP) trained with large datasets of web services QoS values.
References
R.-D. Albu (2013), Contributions regarding the quality and reliability of web services, PhD Thesis, University of Oradea.
R.-D. Albu (2013), Investigating the Effect of Hidden Layers Number on Web Services Response Time Prediction, Nonconventional Technologies Review, ISSN 1454-3087, 7(1):4-9.
R.-D. Albu, I. Felea, F. Popentiu-Vlădicescu (2013), On the Best Adaptive Model for Web Services Response Time Prediction, The 20th Int. Conference on Systems, Signals and Image Processing, IWSSIP 2013, CD Edition, IEEE Catalog Number : CFP1355E-CDR, ISBN: 978-1-4799-0942-1,39-42.
R.-D. Albu, F. Popentiu-Vlădicescu (2013), On the Best Learning Algorithm forWeb Services Response Time Prediction, paper accepted at ESREL "Annual Conference, Advances in Safety, Reliability and Risk Management.
R.-D. Albu, F. Popentiu-Vladicescu (2013), A Comparative Study ForWeb Services Response Time Prediction, The 9th Int. Scientific Conference eLSE 2013 "eLearning and Software for Education", 1: 656-665, Bucharest, ISSN 2006-026x, CD Edition.
A. Klasnja-Milicevic, M. Ivanovic, A. Nanopoulos (2009), The Use of Nonlinear Manifold Learning in Recommender Systems, 4th Int. Conference On Information Technology, http://www.zuj.edu.jo/conferences/ICIT09/PaperList/Papers/AritificialIntelligence/525.pdf.
http://arxiv.org/abs/1111.4503 (available 16.11.2013)
http://blogs.sas.com/content/sascom/2012/04/11/will-big-data-and-high-performanceanalytics-flatten-the-world/ (22.10.2013)
http://tech.sina.com.cn/i/2012-11-12/00207788375.shtml (available 16.11.2013)
http://www.datadeluge.com/ (available 12.11.2013)
L. Aspirot, P. Belzarena, B. Bazzano, G. Perera (2005), End-to-end quality of service prediction based on functional regression, Proc. of Third Int. Working Conference on Performance Modelling and Evaluation of Heterogeneous Networks (HET-NETs 2005), Ilkley, UK, 1-8.
Neuro Solutions help: http://www.aertia.com/docs/nd/neurosolutionshelp.pdf.
P. Belzarena and L. Aspirot (2010), End-to-end quality of service seen by applications: A statistical learning approach, Int. J. of Computer and Telecommunications Networking, 54(17):3123-3143.
Hu Y., Mu D., Gao A., Dai G.(2011), The Research of QoS Approach in Web Servers, Int J Comput Commun, ISSN 1841-9836, 6(4):636-647.
Navarro M., Donoso Y. (2012), An IMS Architecture and Algorithm Proposal with QoS Parameters for Flexible Convergent Services with Dynamic Requirements, Int J Comput Commun ISSN 1841-9836, 7(1):123-134.
Park E.-C. et al. (2011), Quality of Service Control for WLAN-based Converged Personal Network Service, Int J Comput Commun, ISSN 1841-9836, 6(4):716-733.
P. Talebi Fard et al. (2013), Semantic Based Networking of Information in Vehicular Clouds Based on Dimensionality Reduction, Proc. of the third ACM int. symposium on Design and analysis of intelligent vehicular networks and applications, ACM, 69-76.
Wei Tan, M. Brian Blake, Iman Saleh, Schahram Dustdar (2013), Social-Network-Sourced Big Data Analytics, Web-Scale Workflow, Internet Computing, IEEE, 17(5):62-69. http://dx.doi.org/10.1109/MIC.2013.100
Liang-Jie Zhang, Jia Zhang, Hong Cai (2007), Services Computing, Tsinghua University Press, Springer.
Z. Zheng, Y. Zhang, M.R. Lyu (2010), Distributed QoS Evaluation for Real-World Web Services, Proc. of the 8th Int. Conference on Web Services (ICWS2010), Miami, Florida, USA, 83-90.
Z. Zheng, Y. Zhang, M.R. Lyu (2011), Exploring Latent Features for Memory-Based QoS Prediction in Cloud Computing, Proc. of the 30th IEEE Symposium on Reliable Distributed Systems (SRDS 2011), Madrid, Spain, 1-7. http://dx.doi.org/10.1109/SRDS.2011.10
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