Abstract: A method of the present invention provides an efficient, two-stage, online method for discovering interactions among components and groups of components, including time-delayed effects, in large production systems. The first stage compresses a set of anomaly signals using a principal component analysis and passes the resulting eigensignals and a small set of other signals to the second stage, a lag correlation detector, which identifies time-delayed correlations. Real use cases are described from eight unmodified production systems.

Abstract: A system for predicting an occurrence of a critical even in a computer cluster includes: a control system that includes an event log, a system parameter log, a memory for storing information related to occurrences of critical events, and a processor. The processor implements a hybrid prediction system; loads the information from the event log and the system performance log into a Bayesian network model; uses the Bayesian network model to predict a future critical event; makes future scheduling and current data migration selections; and adapts the Bayesian network model by feeding the scheduling and data migration selections.

Abstract: In a multi node information processing system, a method for scheduling jobs, includes steps of: determining node-related performance parameters for a plurality of nodes; determining a ranking for each node based on the node related performance parameters for each node; and ordering each nodes by its ranking for job scheduling.

Abstract: A system for predicting an occurrence of a critical even in a computer cluster includes: a control system that includes an event log, a system parameter log, a memory for storing information related to occurrences of critical events, and a processor. The processor implements a hybrid prediction system; loads the information from the event log and the system performance log into a Bayesian network model; uses the Bayesian network model to predict a future critical event; makes future scheduling and current data migration selections; and adapts the Bayesian network model by feeding the scheduling and data migration selections.

Abstract: A hybrid method of predicting the occurrence of future critical events in a computer cluster having a series of nodes records system performance parameters and the occurrence of past critical events. A data filter filters the logged to data to eliminate redundancies and decrease the data storage requirements of the system. Time-series models and rule based classification schemes are used to associate various system parameters with the past occurrence of critical events and predict the occurrence of future critical events. Ongoing processing jobs are migrated to nodes for which no critical events are predicted and future jobs are routed to more robust nodes.

Abstract: Briefly, according to the invention in an information processing system including a plurality of information processing nodes, a request for checkpointing by an application includes node health criteria (or parameters). The system has the authority to grant or deny the checkpointing request depending on the system health or availability. This scheme significantly improves not only the system performance, but also the application running time as the system. By skipping a checkpoint the application can use the same time to run the application instead of spending extra time for checkpointing.