International Business Machines Corporation

Daniela K M Trevisan

Joseph Reyes

Naina Singh

Mark A Lindsay

A machine-learning mechanism of a disaster-avoidance system trains a knowledgebase to associate characteristics of a data-center component with corresponding degrees of vulnerability to failure and with remedial steps that may be undertaken to avoid failure or to reduce adverse effects of a failure. This training is performed as a function of inferences derived from historical records and from extrinsic information sources. The historical records identify past failures of similar components, component characteristics associated with past failures, and results of remedial procedures undertaken in response to past failures or to previous occurrences of the characteristics. The extrinsic sources identify the current existence of external conditions known to be associated with past failures. When a component's total degree of vulnerability exceeds a predefined threshold value, the system assembles a subset of that component's remedial steps into a remedial procedure and directs downstream modules or administrators to implement the procedure.

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Porto Alegre, Brazil

Daniela K M Trevisan