A Machine Learning Middleware For On Demand Grid Services Engineering and Support

Wail M. Omar, A. Taleb-Bendiab, Yasir Karam

2005

Abstract

Over the coming years, many are anticipating grid computing infrastructure, utilities and services to become an integral part of future socio-economical fabric. Though, the realisation of such a vision will be very much affected by a host of factors including; cost of access, reliability, dependability and security of grid services. In earnest, autonomic computing model of systems’ self-adaptation, self-management and self-protection has attracted much interest to improving grid computing technology dependability, security whilst reducing cost of operation. A prevailing design model of autonomic computing systems is one of a goal-oriented and model-based architecture, where rules elicited from domain expert knowledge, domain analysis or data mining are embedded in software management systems to provide autonomic systems functions including; self-tuning and/or self-healing. In this paper, however, we argue for the need for unsupervised machine learning utility and associated middleware to capture knowledge sources to improve deliberative reasoning of autonomic middleware and/or grid infrastructure operation. In particular, the paper presents a machine learning middleware service using the well-known Self-Organising Maps (SOM), which is illustrated through a case-study scenario -- intelligent connected home. The SOM service is used to classify types of users and their respective networked appliances usage model (patterns). The models are accessed by our experimental self-managing infrastructure to provide Just-in-Time deployment and activation of required services in line with learnt usage models and baseline architecture of specified services assemblies. The paper concludes with an evaluation and general concluding remarks.

References

  1. V. Berstis, Fundamentals of Grid Computing, 2002.
  2. M. Y. Chen, E. Kiciman, E. Fratkin, A. Fox, and E. Brewer, Pinpoint: Problem Determination in Large, Dynamic Internet Services, 2002.
  3. M. LaMonica. IBM draws self-management blueprint, April 2003.
  4. M. Chen, A. X. Zheng, J. Lloyd, M. I. Jordan and E. Brewer, Failure Diagnosis Using Decision Trees. 2004.
  5. G. Candea, M. Delgado, M. Chen and A. Fox, Automatic Failure-Path Inference: A Generic Introspection Technique for Internet Applications, June 2003.
  6. M. Y. Chen, A. Accardi, E. Kiciman, J. Lloyd, D. Patterson, A. Fox and E. Brewer, PathBased Failure and Evolution Management, 2003
  7. U. Heuser, J. Goppert, W. Rosenstiel and A. Stevens, Classification of Human Brain Waves using Self-Organizing Maps, August 1996
  8. N. N. Schraudolph and T. J. Sejnowski, Competitive Anti-Hebbian Learning of Invariants, 1992.
  9. R. H. White, Competitive Hebbian Learning 2: an Introduction, 1992.
  10. J. Vesanto, Using SOM in Data Mining. Licentiate's thesis. Thesis for the degree of Licentiate of Science in Technology, Supervisors: Professor Olli Simula, Professor Samuel Kaski, Espoo, Finland 17th April 2000.
  11. R. Pollock, T. Lane and M. Watts, A Kohonen Self-Organizing Map for the functional classification of proteins based on one-dimensional sequence information, 2001.
  12. E. Bingham, J. Kuusisto and K. Lagus, ICA and SOM in Text Document Analysis, August, 2002.
  13. Haynos, J.U.a.M., A visual tour of Open Grid Services Architecture, August 2003.
  14. J. Vesanto, J. Himberg, E. Alhoniemi and J. Parhankangas, Self-Organizing Map in Matlab: the SOM Toolbox. In proceedings of the Matlab DSP Conference 1999, Espoo, Finland, pp. 35-40, 1999.
  15. T. Joachims, Text Categorization with Support Vector Machine: Learning With Many Relevant Features. ECML-98. 10th European Conference on Machine Learning, Heidelberg, Germany, 1998.
  16. C. Hsu, C. Chang, and C. Lin, A Practical Guide to Support Vector Classification, 2003.
  17. D. Arnaud, Study of a document classification framework using Self-Organizing Maps, October 1, 2003
  18. T. Kohonen, The Self-Organizing Map (SOM). October, 2000. http://www.cis.hut.fi/projects/somtoolbox/theory/somalgorithm.shtml
  19. http://www.cs.toronto.edu/delve/methods/knn-class-1/knn-class-1.html
Download


Paper Citation


in Harvard Style

M. Omar W., Taleb-Bendiab A. and Karam Y. (2005). A Machine Learning Middleware For On Demand Grid Services Engineering and Support . In Proceedings of the 2nd International Workshop on Computer Supported Activity Coordination - Volume 1: CSAC, (ICEIS 2005) ISBN 972-8865-21-X, pages 89-100. DOI: 10.5220/0002558200890100


in Bibtex Style

@conference{csac05,
author={Wail M. Omar and A. Taleb-Bendiab and Yasir Karam},
title={A Machine Learning Middleware For On Demand Grid Services Engineering and Support},
booktitle={Proceedings of the 2nd International Workshop on Computer Supported Activity Coordination - Volume 1: CSAC, (ICEIS 2005)},
year={2005},
pages={89-100},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0002558200890100},
isbn={972-8865-21-X},
}


in EndNote Style

TY - CONF
JO - Proceedings of the 2nd International Workshop on Computer Supported Activity Coordination - Volume 1: CSAC, (ICEIS 2005)
TI - A Machine Learning Middleware For On Demand Grid Services Engineering and Support
SN - 972-8865-21-X
AU - M. Omar W.
AU - Taleb-Bendiab A.
AU - Karam Y.
PY - 2005
SP - 89
EP - 100
DO - 10.5220/0002558200890100