Authors:
Wail M. Omar
1
;
A. Taleb-Bendiab
2
and
Yasir Karam
2
Affiliations:
1
School of Computing and Mathematical Sciences, Liverpool John Moores University; Faculty of Applied Sciences, Sohar University, Oman
;
2
School of Computing and Mathematical Sciences, Liverpool John Moores University, United Kingdom
Related
Ontology
Subjects/Areas/Topics:
Artificial Intelligence
;
Artificial Intelligence and Decision Support Systems
;
Coordination in Multi-Agent Systems
;
Databases and Information Systems Integration
;
Enterprise Information Systems
;
Enterprise Resource Planning
;
Enterprise Software Technologies
;
Information Systems Analysis and Specification
;
Knowledge Management and Information Sharing
;
Knowledge-Based Systems
;
Semiotics
;
Simulation and Modeling
;
Simulation Tools and Platforms
;
Software Engineering
;
Symbolic Systems
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 middlewar
e 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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