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METHOD FOR INTELLIGENT PATCH SCHEDULING USING HISTORIC AVERAGES OF VIRTUAL I/O UTILIZATION AND PREDICTIVE MODELING

  • US 20090187899A1
  • Filed: 01/23/2008
  • Published: 07/23/2009
  • Est. Priority Date: 01/23/2008
  • Status: Abandoned Application
First Claim
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1. A method for intelligent patch scheduling for a virtual input and output (I/O) server comprising:

  • monitoring virtual I/O performance indicators of a virtual I/O server;

    storing the performance indicators in a database of the virtual I/O server;

    maintaining historic averages of the performance indicators in the database;

    receiving patches to be applied to a client partition of the virtual I/O server;

    receiving a reboot window for the client partition, wherein the reboot window is an allowed time frame for rebooting the virtual I/O server to apply the patches;

    predicting future virtual I/O utilization by running predictive modeling utilizing the historic averages of the performance indicators, wherein based on the predictive modeling, a module determines and selects a specific time within the allowed time frame for rebooting the client partition of the virtual I/O server to apply the patches;

    wherein predictive modeling comprises;

    selecting a utilization range from a plurality of utilization ranges for the virtual I/O server;

    selecting a time window from a plurality of time windows for the virtual I/O server;

    by utilizing the historic averages of the performance indicators, determining a probability that the virtual I/O server should be rebooted during the selected utilization range and determining a probability that the virtual I/O server should be rebooted during the selected time window;

    to equal a total YES probability, combining the probability that the virtual I/O server should be rebooted during the selected utilization range with the probability that the virtual I/O server should be rebooted during the selected time window;

    by utilizing the historic averages of the performance indicators, determining a probability that the virtual I/O server should not be rebooted during the selected utilization range and determining a probability that the virtual I/O server should not be rebooted during the selected time window;

    to equal a total NO probability, combining the probability that the virtual I/O server should not be rebooted during the selected utilization range with the probability that the virtual I/O server should not be rebooted during the selected time window; and

    comparing the total YES probability to the total NO probability; and

    rebooting the client partition of the virtual I/O server to apply the patches at the specific time within the reboot window, responsive to the total YES probability being greater than the total NO probability.

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