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

Abstract

Over the coming years, many are anticipating grid computing infrastructure, utilities and services to become an integral part of future socioeconomical 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 casestudy 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.