A Dimension Separation Based Hybrid Classifier Ensemble for Locating Faults in Cloud Services
AbstractCloud services provide Internet users with various services featured with data fusion through the dynamic and expandable virtual resources. Because a large amount of data runs in different modules of the cloud service systems, it will inevitably produce all kinds of failures when the data is processed in and transferred between modules. Therefore the job of rapid fault location has an important role in improving the quality of cloud services. Because of the features of large scale and data fusion of data in the cloud service system, it is difficult to use the conventional fault locating method to locate the faults quickly. Taking the requirements on the speed of locating faults into account, we will make a clear division to all possible failure causes according to the business phases, and quickly locate the faults by implementing a cascading structure of the neural network ensemble. At last, we conducted an experiment of locating faults in a cloud service system runned by a telecom operator, comparing the proposed hybird classifier ensemble with neural networks trained by separated data subsets and a conventional neural network ensemble based on bagging algorithm. The experiment proved that the neural network ensemble based on dimension separation is effective for locating faults in cloud services.
 Noor, T.H., Sheng, Q.Z., Ngu, A.H.H., Dustdar, S. (2014); Analysis of Web-Scale Cloud Services, IEEE Internet Computing, ISSN 1089-7801, 18(4): 55-61.
 Breiter, G., Behrendt, M. (2009); Life Cycle and Characteristics of Services in the World of Cloud Computing, IBM Journal of Research and Development, ISSN 0018-8646, 53(4): 1-8.
 Gu, Y., Wang, D.S., Liu, C.Y. (2014); DR-Cloud: Multi-Cloud Based Disaster Recovery Service, Tsinghua Science and Technology, ISSN 1007-0214, 9(1): 1-13.
 Chauvel, F., Song, H., Ferry, N., Fleurey, F. (2015); Evaluating Robustness of Cloud-Based Systems, Journal of Cloud Computing: Advances, Systems and Applications, ISSN 2192- 113X, 4(18): 1-17.
 Ren, J. (2012); ANN vs. SVM: Which One Performs Better in Classification of MCCs in Mammogram Imaging, Knowledge-Based Systems, ISSN 0950-7051, 26(1): 144-153.
 Ahn, B.S., Cho, S.S., Kim, C.Y. (2000); The Integrated Methodology of Rough Set Theory and Artificial Neural Network for Business Failure Prediction, Expert Systems with Applications, ISSN 0957-4174, 18(2): 65-74.
 Webb, G.I., Zheng, Z. (2004); Multistrategy Ensemble Learning: Reducing Error by Combining Ensemble Learning Techniques, IEEE Transactions on Knowledge and Data Engineering, ISSN 1041-4347, 16(8): 980-991.
 Zhou, Z.H., Wu, J.X., Tang, W. (2002); Ensembling Neural Networks: Many Could Be Better Than All, Artificial Intelligence, ISSN 0004-3702, 137(1-2): 239-263.
 Perrone, M.P., Cooper, L.N.(1993);
When Networks Disagree: Ensemble Method for Neural Networks. In: Mammone, R.J. (ed.) Artificial Neural Networks for Speech and Vision, Chapman & Hall, New York, ISBN 978-041-25-4850-5.
 Mui, J.K., Fu, K.S.(2014); Automated Classification of Nucleated Blood Cells Using a Binary Tree Classifier, IEEE Transactions on Pattern Analysis and Machine Intelligence, ISSN 0162-8828, 2(5): 429-443.
 Hosseini, K.(2015); Accurate Hybrid Method for Rapid Fault Detection, Classification and Location in Transmission Lines using Wavelet Transform and ANNs, International Journal of Scientific Engineering and Technology, ISSN 2277-1581, 4(5): 329-334.
 Dounias, G., Linkens, D. (2004); Adaptive Systems and Hybrid Computational Intelligence in Medicine, Artificial Intelligence in Medicine, ISSN 0933-3657, 32(3): 151-155.
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
ONLINE OPEN ACCES: Acces to full text of each article and each issue are allowed for free in respect of Attribution-NonCommercial 4.0 International (CC BY-NC 4.0.
You are free to:
-Share: copy and redistribute the material in any medium or format;
-Adapt: remix, transform, and build upon the material.
The licensor cannot revoke these freedoms as long as you follow the license terms.
DISCLAIMER: The author(s) of each article appearing in International Journal of Computers Communications & Control is/are solely responsible for the content thereof; the publication of an article shall not constitute or be deemed to constitute any representation by the Editors or Agora University Press that the data presented therein are original, correct or sufficient to support the conclusions reached or that the experiment design or methodology is adequate.