Patent ID: 7877234

Claim:
A method for monitoring and analyzing a computational cluster for one or more conditions that indicate an impending malfunction comprising: receiving values of one or more attributes evinced in the computational cluster, wherein the computational cluster comprises a collection of a statistically significant number of statistically similar system components; providing one or more abstract probabilistic models for modeling a set V of the one or more attribute values, wherein the one or more abstract probabilistic models include one or more parameters each having a numeric value, and wherein the abstract probabilistic models are generated by a user or selected from a set of typical models based on a set of imposed constraints or some combination thereof; probabilistically inferring numeric values of the one or more parameters for each of the one or more abstract probabilistic models modeling the set V through an iterative process comprising a variable number of Bayesian inference iterations until the iterative process converges to a predetermined convergence criterion, wherein each of the one or more abstract probabilistic models when combined with a single inferred number for each of the parameters is defined as a concrete probabilistic model; determining relative probabilities of at least some of the one or more attributes evinced in the computational cluster; providing criteria for one or more conditions that indicate a malfunction or that may lead to a malfunction based upon the relative probabilities of the at least some of the one or more attributes evinced in the computational cluster and the one or more concrete probabilistic models; determining whether any of the one or more conditions that indicate a malfunction or that may lead to a malfunction are met; and providing a signal and information identifying which of the one or more conditions that indicate a malfunction or that may lead to a malfunction are met.