David A Homoki

7280988, Oct 9, 2007

Dec 19, 2002

10/324641

David Helsper - Marietta GA, US
Jean-Francois Huard - New York NY, US
David Homoki - Marietta GA, US
Amanda Rasmussen - Atlanta GA, US
Robert Jannarone - San Diego CA, US

Netuitive, Inc. - Reston VA

G06E 1/00

706 26, 706 22, 706 21, 709224, 702182, 702186

A monitoring system including a baseline model that automatically captures and models normal system behavior, a correlation model that employs multivariate autoregression analysis to detect abnormal system behavior, and an alarm service that weights and scores a variety of alerts to determine an alarm status and implement appropriate response actions. The baseline model decomposes the input variables into a number of components representing relatively predictable behaviors so that the erratic component e(t) may be isolated for further processing. These components include a global trend component, a cyclical component, and a seasonal component. Modeling and continually updating these components separately permits a more accurate identification of the erratic component of the input variable, which typically reflects abnormal patterns when they occur.

20020188582, Dec 12, 2002

Jun 7, 2002

10/165232

Robert Jannarone - San Diego CA, US
David Homoki - Marietta GA, US
Amanda Rasmussen - Atlanta GA, US

G06E001/00

706/026000

This invention specifies analyzers to be run in conjunction with computer estimation systems, which may be applied to performance monitoring (APM) services. A semi-automated analyzer may be used by a human analyst to periodically evaluate available historical data for establishing a desired set of input measurements, model parameters, and reporting criteria, collectively called configuration parameters, to be used by an estimation system. In addition, a fully automated analyzer may periodically and automatically reevaluate such configuration parameters and automatically reconfigure the estimation system accordingly. For both types of analyzer, the active set of input measurements for the computerized estimation system can be initially established or periodically updated to perform any variety of configuration tuning operations, including the following: removing linearly redundant variables; removing inputs showing no variation; removing unnecessary or non-estimable inputs; tuning estimation system operating parameters that govern learning rates and the use of recent trends; occasional model accuracy assessment; and tuning monitoring alarm thresholds.