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Clustering process for software server failure prediction

  • US 7,484,132 B2
  • Filed: 10/28/2005
  • Issued: 01/27/2009
  • Est. Priority Date: 10/28/2005
  • Status: Expired due to Fees
First Claim
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1. A computer-implemented method for predicting failure of a server. comprising:

  • creating cluster profiles;

    collecting real time server parameters of the server;

    applying the real time server parameters to at least one of the cluster profiles, wherein the cluster profile comprises;

    one or more server parameters;

    one or more clustering parameters; and

    a weight associated with each server parameter,wherein the server parameters, the clustering parameters, and the weight associated with each server parameter are selected on the basis of historical pre-fault clustering of the server parameters; and

    determining a probability of failure of the server based on a relationship between the real time server parameters and the one or more cluster profiles;

    wherein creating the cluster profiles, comprising;

    for each historical server failure, collecting data for server parameters for a period of time immediately preceding the historical server failure;

    determining at least one setting, wherein the setting comprises;

    the one or more server parameters;

    the one or more clustering parameters; and

    the weight associated with each of the one or more server parameters;

    determining a set of points, wherein each point comprises values of the one or more server parameter;

    determining clusters of points within the set of points based on the distance between each pair of points in the set of points, the one or more clustering parameters, and the weight associated with each server parameter;

    for each setting, determining whether the setting generates a pre-fault clustering pattern, wherein the pre-fault clustering pattern comprises at least one of;

    a high rate of change of clusters prior to the server failure; and

    clusters previously defined as high-risk clusters; and

    if the setting generates a pre-fault clustering pattern, saving the setting as a cluster profile.

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