Hybrid event prediction and system control
AssigneeInternational Business Machines Corporation
InventorsManish Gupta, Jose E. Moreira, Adam J. Oliner, Ramendra K. Sahoo
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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.
Claims18
An information processing system for predicting an occurrence of a critical event in a computer cluster, the information processing system comprising:
- the computer cluster comprising a series of nodes;
- a control system comprising:
- A) an event log comprising information concerning critical events that occur in the computer cluster, wherein said critical events adversely affect performance of the cluster or one of its nodes;
- B) a system parameter log comprising information concerning system performance parameters for each node in the cluster;
- C) a processor configured for:
- i) implementing a hybrid prediction system comprising rule based prediction algorithms, time-dependent variable prediction algorithms, and a warning window;
- ii) maintaining the event log for only those nodes in which an error has occurred;
- iii) maintaining the system parameter log for each node in the cluster;
- iv) for only those nodes in which an error has occurred, loading the information from the event log and the system performance information pertaining to said error-prone nodes from the system parameter log into a Bayesian network model representing a correspondence between the system performance parameters and occurrence of the critical events;
- v) using the Bayesian network model to predict a future critical event within a specified time-limit based upon the hybrid prediction system;
- vi) making future scheduling and current data migration selections based upon the hybrid prediction system;
- and
- vii) adapting the Bayesian Network Model by feeding the scheduling and data migration selections into said Bayesian Network Model;
- and
- D) a memory for storing information related to said occurrence of said critical events and said system parameters of said nodes.
A non-transitory computer readable storage medium comprising program instructions that predicts an occurrence of a critical event in a computer cluster by:
- implementing a hybrid prediction system comprising rule based prediction algorithms, time-dependent variable prediction algorithms, and a warning window for a computer cluster comprising a series of nodes, said hybrid prediction system carrying out instructions for:
- maintaining an event log for only those nodes in which an error has occurred, wherein the error adversely affects performance of the cluster or one of its nodes;
- maintaining a system parameter log for each node in the computer cluster;
- for only those nodes in which an error has occurred, loading the information from the event log into a Bayesian network model representing a correspondence between the system performance parameters and occurrence of the critical events;
- for only those nodes in which an error has occurred, loading system performance information pertaining to said error-prone nodes from a system parameter log comprising information concerning system performance parameters for each node in the cluster into the Bayesian network model;
- using the Bayesian network model to predict a future critical event within a specified time-limit based upon the hybrid prediction system;
- storing information related to said occurrence of said critical events and said system parameters of said nodes;
- making future scheduling and current data migration selections based upon the hybrid prediction system;
- and
- adapting the Bayesian Network Model by feeding the scheduling and data migration selections into said Bayesian Network Model.
Dependencies are inferred from the claim text.