Abstract: A plurality of stochastic models is built that predict the probabilities of state transitions for components in a complex system. The models are trained using output observations from the system at runtime. The overall state and health of the system can be determined at runtime by analyzing the distribution of current component states among the possible states. Subsequent to a low level component failure, the state transition probability stochastic model for the failed component can be analyzed by uncovering the previous states at N time intervals prior to the failure. The resulting state transition path for the component can be analyzed for the causes of the failure. Additionally, component failures resulting from the failure, or worsening state transition, in other components can be diagnosed by uncovering the previous states at the N times prior to the failure for multiple components in the system and then analyzing the state transition paths for correlations to the failed component.

Abstract: A plurality of causal ladder is assembled in advance from component system events taken from previous system failures. The ladders classify the various transitions the system goes through from one set of observed states to another in multiple stages representing issues of differing urgency, importance and need for remediation. These stages are used at runtime determine the criticality of any abnormal system activity and to accurately predict the component failure prior to the system crashing. Each ladder comprises a plurality of elevated stages representing criticality of the problem. At runtime, the causal ladder engine correlates real-time events received from the system to stages of one or more pre-constructed causal ladders and identifies a probable problem (and/or the faulty component) from the corresponding causal ladder. The causal ladder engine also determines the stage of the problem from event occurrences. At each stage, a different potential solution is identified for the problem.

Abstract: A plurality of causal ladder is assembled in advance from component system events taken from previous system failures. The ladders classify the various transitions the system goes through from one set of observed states to another in multiple stages representing issues of differing urgency, importance and need for remediation. These stages are used at runtime to determine the criticality of any abnormal system activity and to accurately predict the component failure prior to the system crashing. Each ladder comprises a plurality of elevated stages representing criticality of the problem. At runtime, the causal ladder engine correlates real-time events received from the system to stages of one or more pre-constructed causal ladders and identifies a probable problem (and/or the faulty component) from the corresponding causal ladder. The causal ladder engine also determines the stage of the problem from event occurrences. At each stage, a different potential solution is identified for the problem.