Resource use pattern analysis for predicting resource availability in opportunistic grids

This work presents a method for predicting resource availability in opportunistic grids by means of Use Pattern Analysis (UPA), a technique based on non-supervised learning methods. This prediction method is based on the assumption of the existence of several classes of computational resource use patterns, which can be used to predict resource availability. Trace-driven simulations validate this basic assumptions, which also provide the parameter settings for the accurate learning of resource use patterns. Experiments made with an implementation of the UPA method show the feasibility of its use in the scheduling of grid tasks with very little overhead. The experiments also demonstrate the method’s superiority over other predictive and non-predictive methods. An adaptative prediction method is suggested to deal with lack of training data at initialisation. Further adaptative behaviour is motivated by experiments which show that, in some special environments, reliable resource use patterns may not always be detected.