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

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

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.

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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