The patent badge is an abbreviated version of the USPTO patent document. The patent badge does contain a link to the full patent document.
The patent badge is an abbreviated version of the USPTO patent document. The patent badge covers the following: Patent number, Date patent was issued, Date patent was filed, Title of the patent, Applicant, Inventor, Assignee, Attorney firm, Primary examiner, Assistant examiner, CPCs, and Abstract. The patent badge does contain a link to the full patent document (in Adobe Acrobat format, aka pdf). To download or print any patent click here.
Patent No.:
Date of Patent:
Nov. 16, 2010
Filed:
Apr. 28, 2008
Peter J. Haas, San Jose, CA (US);
John M. Lake, Cary, NC (US);
Guy M. Lohman, San Jose, CA (US);
Ashutosh Singh, San Jose, CA (US);
Tanveer F. Syeda-mahmood, Cupertino, CA (US);
Peter J. Haas, San Jose, CA (US);
John M. Lake, Cary, NC (US);
Guy M. Lohman, San Jose, CA (US);
Ashutosh Singh, San Jose, CA (US);
Tanveer F. Syeda-Mahmood, Cupertino, CA (US);
International Business Machines Corporation, Armonk, NY (US);
Abstract
A method for monitoring dependent metric streams for anomalies including identifying a plurality of sets of dependent metric streams from a plurality of metric streams of a computer system by measuring an association of the plurality of metric streams using a statistical dependency measure analysis, wherein each set includes a plurality of the dependent metric streams and each metric stream includes a plurality of data, determining a subset of the plurality of sets of dependent metric streams to monitor by selecting a quantity of the sets of dependent metric streams that have a highest statistical dependency, cleaning the data of each set of dependent metric streams of the subset by identifying and removing outlier data, fitting a probability density function to the cleaned data of each set of dependent metric streams of the subset, wherein the probability density function is a likelihood function that provides a likelihood of an occurrence of the cleaned data, determining a detection threshold that is a lower threshold on the likelihood of the occurrence of the cleaned data of each set of dependent metric streams of the subset based on the likelihood function, detecting an anomaly if a likelihood of an occurrence of a new data of one of the sets of dependent metric streams of the subset is less than the detection threshold, and transmitting an alert signal in response to detecting the anomaly.